├── googled873bf132668a2f1.html ├── css ├── theme.css ├── theme.dark.css ├── theme.light.css ├── colors.module.css ├── styles.css ├── typography.module.css └── tokens.css ├── sitemap.xml ├── LICENSE ├── CONTRIBUTING.md └── README.md /googled873bf132668a2f1.html: -------------------------------------------------------------------------------- 1 | google-site-verification: googled873bf132668a2f1.html -------------------------------------------------------------------------------- /css/theme.css: -------------------------------------------------------------------------------- 1 | @import url(tokens.css); 2 | @import url(colors.module.css); 3 | @import url(typography.module.css); 4 | @import url(theme.light.css) (prefers-color-scheme: light); 5 | @import url(theme.dark.css) (prefers-color-scheme: dark); 6 | -------------------------------------------------------------------------------- /sitemap.xml: -------------------------------------------------------------------------------- 1 | 2 | 7 | 8 | 9 | 10 | 11 | https://guang000.github.io/Awesome-Dataset-Distillation/ 12 | 2024-03-19T12:19:46+00:00 13 | 14 | 15 | 16 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2022 Guang Li, Bo Zhao, and Tongzhou Wang 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /CONTRIBUTING.md: -------------------------------------------------------------------------------- 1 | # Contribution Guide 2 | 3 | 1. Put the bibtex in `Awesome-Dataset-Distillation/citations/.txt`. 4 | 2. In the `README.md`, add the paper to **one** proper section in the format `TITLE (AUTHOR, CONFERENCE YEAR) OPTIONAL_PROJECT_LINK OPTIONAL_GITHUB BIBTEX`. 5 | 6 | + Title should be a link to the paper. When the paper is hosted on arXiv, please use the abstract link (rather than pdf link). 7 | 8 | + Change the number in "Papers-number-FF6F00". 9 | 10 | + Use the following icons to represent otherlinks: 11 | + :globe_with_meridians: (`:globe_with_meridians:`) Project Page 12 | + :octocat: (`:octocat:`) Code 13 | + :book: (`:book:`) `bibtex` 14 | 15 | For example: 16 | 17 | + [Dataset Distillation](https://arxiv.org/abs/1811.10959) (Tongzhou Wang et al., 2018) [:globe_with_meridians:](https://www.tongzhouwang.info/dataset_distillation/) [:octocat:](https://github.com/SsnL/dataset-distillation) [:book:](./citations/wang2018datasetdistillation.txt) 18 | 19 | 20 | Above entry is generated using following Markdown: 21 | 22 | ``` 23 | + [Dataset Distillation](https://arxiv.org/abs/1811.10959) (Tongzhou Wang et al., 2018) 24 | [:globe_with_meridians:](https://www.tongzhouwang.info/dataset_distillation/) 25 | [:octocat:](https://github.com/SsnL/dataset-distillation) 26 | [:book:](./citations/wang2018datasetdistillation.txt) 27 | 28 | ``` 29 | -------------------------------------------------------------------------------- /css/theme.dark.css: -------------------------------------------------------------------------------- 1 | :root { 2 | --md-sys-color-primary: var(--md-sys-color-primary-dark); 3 | --md-sys-color-on-primary: var(--md-sys-color-on-primary-dark); 4 | --md-sys-color-primary-container: var(--md-sys-color-primary-container-dark); 5 | --md-sys-color-on-primary-container: var(--md-sys-color-on-primary-container-dark); 6 | --md-sys-color-secondary: var(--md-sys-color-secondary-dark); 7 | --md-sys-color-on-secondary: var(--md-sys-color-on-secondary-dark); 8 | --md-sys-color-secondary-container: var(--md-sys-color-secondary-container-dark); 9 | --md-sys-color-on-secondary-container: var(--md-sys-color-on-secondary-container-dark); 10 | --md-sys-color-tertiary: var(--md-sys-color-tertiary-dark); 11 | --md-sys-color-on-tertiary: var(--md-sys-color-on-tertiary-dark); 12 | --md-sys-color-tertiary-container: var(--md-sys-color-tertiary-container-dark); 13 | --md-sys-color-on-tertiary-container: var(--md-sys-color-on-tertiary-container-dark); 14 | --md-sys-color-error: var(--md-sys-color-error-dark); 15 | --md-sys-color-error-container: var(--md-sys-color-error-container-dark); 16 | --md-sys-color-on-error: var(--md-sys-color-on-error-dark); 17 | --md-sys-color-on-error-container: var(--md-sys-color-on-error-container-dark); 18 | --md-sys-color-background: var(--md-sys-color-background-dark); 19 | --md-sys-color-on-background: var(--md-sys-color-on-background-dark); 20 | --md-sys-color-surface: var(--md-sys-color-surface-dark); 21 | --md-sys-color-on-surface: var(--md-sys-color-on-surface-dark); 22 | --md-sys-color-surface-variant: var(--md-sys-color-surface-variant-dark); 23 | --md-sys-color-on-surface-variant: var(--md-sys-color-on-surface-variant-dark); 24 | --md-sys-color-outline: var(--md-sys-color-outline-dark); 25 | --md-sys-color-inverse-on-surface: var(--md-sys-color-inverse-on-surface-dark); 26 | --md-sys-color-inverse-surface: var(--md-sys-color-inverse-surface-dark); 27 | --md-sys-color-inverse-primary: var(--md-sys-color-inverse-primary-dark); 28 | --md-sys-color-shadow: var(--md-sys-color-shadow-dark); 29 | --md-sys-color-surface-tint: var(--md-sys-color-surface-tint-dark); 30 | --md-sys-color-outline-variant: var(--md-sys-color-outline-variant-dark); 31 | --md-sys-color-scrim: var(--md-sys-color-scrim-dark); 32 | } 33 | -------------------------------------------------------------------------------- /css/theme.light.css: -------------------------------------------------------------------------------- 1 | :root { 2 | --md-sys-color-primary: var(--md-sys-color-primary-light); 3 | --md-sys-color-on-primary: var(--md-sys-color-on-primary-light); 4 | --md-sys-color-primary-container: var(--md-sys-color-primary-container-light); 5 | --md-sys-color-on-primary-container: var(--md-sys-color-on-primary-container-light); 6 | --md-sys-color-secondary: var(--md-sys-color-secondary-light); 7 | --md-sys-color-on-secondary: var(--md-sys-color-on-secondary-light); 8 | --md-sys-color-secondary-container: var(--md-sys-color-secondary-container-light); 9 | --md-sys-color-on-secondary-container: var(--md-sys-color-on-secondary-container-light); 10 | --md-sys-color-tertiary: var(--md-sys-color-tertiary-light); 11 | --md-sys-color-on-tertiary: var(--md-sys-color-on-tertiary-light); 12 | --md-sys-color-tertiary-container: var(--md-sys-color-tertiary-container-light); 13 | --md-sys-color-on-tertiary-container: var(--md-sys-color-on-tertiary-container-light); 14 | --md-sys-color-error: var(--md-sys-color-error-light); 15 | --md-sys-color-error-container: var(--md-sys-color-error-container-light); 16 | --md-sys-color-on-error: var(--md-sys-color-on-error-light); 17 | --md-sys-color-on-error-container: var(--md-sys-color-on-error-container-light); 18 | --md-sys-color-background: var(--md-sys-color-background-light); 19 | --md-sys-color-on-background: var(--md-sys-color-on-background-light); 20 | --md-sys-color-surface: var(--md-sys-color-surface-light); 21 | --md-sys-color-on-surface: var(--md-sys-color-on-surface-light); 22 | --md-sys-color-surface-variant: var(--md-sys-color-surface-variant-light); 23 | --md-sys-color-on-surface-variant: var(--md-sys-color-on-surface-variant-light); 24 | --md-sys-color-outline: var(--md-sys-color-outline-light); 25 | --md-sys-color-inverse-on-surface: var(--md-sys-color-inverse-on-surface-light); 26 | --md-sys-color-inverse-surface: var(--md-sys-color-inverse-surface-light); 27 | --md-sys-color-inverse-primary: var(--md-sys-color-inverse-primary-light); 28 | --md-sys-color-shadow: var(--md-sys-color-shadow-light); 29 | --md-sys-color-surface-tint: var(--md-sys-color-surface-tint-light); 30 | --md-sys-color-outline-variant: var(--md-sys-color-outline-variant-light); 31 | --md-sys-color-scrim: var(--md-sys-color-scrim-light); 32 | } 33 | -------------------------------------------------------------------------------- /css/colors.module.css: -------------------------------------------------------------------------------- 1 | .primary { 2 | background-color: var(--md-sys-color-primary); 3 | } 4 | .primary-text { 5 | color: var(--md-sys-color-primary); 6 | } 7 | .on-primary { 8 | background-color: var(--md-sys-color-on-primary); 9 | } 10 | .on-primary-text { 11 | color: var(--md-sys-color-on-primary); 12 | } 13 | .primary-container { 14 | background-color: var(--md-sys-color-primary-container); 15 | } 16 | .primary-container-text { 17 | color: var(--md-sys-color-primary-container); 18 | } 19 | .on-primary-container { 20 | background-color: var(--md-sys-color-on-primary-container); 21 | } 22 | .on-primary-container-text { 23 | color: var(--md-sys-color-on-primary-container); 24 | } 25 | .secondary { 26 | background-color: var(--md-sys-color-secondary); 27 | } 28 | .secondary-text { 29 | color: var(--md-sys-color-secondary); 30 | } 31 | .on-secondary { 32 | background-color: var(--md-sys-color-on-secondary); 33 | } 34 | .on-secondary-text { 35 | color: var(--md-sys-color-on-secondary); 36 | } 37 | .secondary-container { 38 | background-color: var(--md-sys-color-secondary-container); 39 | } 40 | .secondary-container-text { 41 | color: var(--md-sys-color-secondary-container); 42 | } 43 | .on-secondary-container { 44 | background-color: var(--md-sys-color-on-secondary-container); 45 | } 46 | .on-secondary-container-text { 47 | color: var(--md-sys-color-on-secondary-container); 48 | } 49 | .tertiary { 50 | background-color: var(--md-sys-color-tertiary); 51 | } 52 | .tertiary-text { 53 | color: var(--md-sys-color-tertiary); 54 | } 55 | .on-tertiary { 56 | background-color: var(--md-sys-color-on-tertiary); 57 | } 58 | .on-tertiary-text { 59 | color: var(--md-sys-color-on-tertiary); 60 | } 61 | .tertiary-container { 62 | background-color: var(--md-sys-color-tertiary-container); 63 | } 64 | .tertiary-container-text { 65 | color: var(--md-sys-color-tertiary-container); 66 | } 67 | .on-tertiary-container { 68 | background-color: var(--md-sys-color-on-tertiary-container); 69 | } 70 | .on-tertiary-container-text { 71 | color: var(--md-sys-color-on-tertiary-container); 72 | } 73 | .error { 74 | background-color: var(--md-sys-color-error); 75 | } 76 | .error-text { 77 | color: var(--md-sys-color-error); 78 | } 79 | .error-container { 80 | background-color: var(--md-sys-color-error-container); 81 | } 82 | .error-container-text { 83 | color: var(--md-sys-color-error-container); 84 | } 85 | .on-error { 86 | background-color: var(--md-sys-color-on-error); 87 | } 88 | .on-error-text { 89 | color: var(--md-sys-color-on-error); 90 | } 91 | .on-error-container { 92 | background-color: var(--md-sys-color-on-error-container); 93 | } 94 | .on-error-container-text { 95 | color: var(--md-sys-color-on-error-container); 96 | } 97 | .background { 98 | background-color: var(--md-sys-color-background); 99 | } 100 | .background-text { 101 | color: var(--md-sys-color-background); 102 | } 103 | .on-background { 104 | background-color: var(--md-sys-color-on-background); 105 | } 106 | .on-background-text { 107 | color: var(--md-sys-color-on-background); 108 | } 109 | .surface { 110 | background-color: var(--md-sys-color-surface); 111 | } 112 | .surface-text { 113 | color: var(--md-sys-color-surface); 114 | } 115 | .on-surface { 116 | background-color: var(--md-sys-color-on-surface); 117 | } 118 | .on-surface-text { 119 | color: var(--md-sys-color-on-surface); 120 | } 121 | .surface-variant { 122 | background-color: var(--md-sys-color-surface-variant); 123 | } 124 | .surface-variant-text { 125 | color: var(--md-sys-color-surface-variant); 126 | } 127 | .on-surface-variant { 128 | background-color: var(--md-sys-color-on-surface-variant); 129 | } 130 | .on-surface-variant-text { 131 | color: var(--md-sys-color-on-surface-variant); 132 | } 133 | .outline { 134 | background-color: var(--md-sys-color-outline); 135 | } 136 | .outline-text { 137 | color: var(--md-sys-color-outline); 138 | } 139 | .inverse-on-surface { 140 | background-color: var(--md-sys-color-inverse-on-surface); 141 | } 142 | .inverse-on-surface-text { 143 | color: var(--md-sys-color-inverse-on-surface); 144 | } 145 | .inverse-surface { 146 | background-color: var(--md-sys-color-inverse-surface); 147 | } 148 | .inverse-surface-text { 149 | color: var(--md-sys-color-inverse-surface); 150 | } 151 | .inverse-primary { 152 | background-color: var(--md-sys-color-inverse-primary); 153 | } 154 | .inverse-primary-text { 155 | color: var(--md-sys-color-inverse-primary); 156 | } 157 | .shadow { 158 | background-color: var(--md-sys-color-shadow); 159 | } 160 | .shadow-text { 161 | color: var(--md-sys-color-shadow); 162 | } 163 | .surface-tint { 164 | background-color: var(--md-sys-color-surface-tint); 165 | } 166 | .surface-tint-text { 167 | color: var(--md-sys-color-surface-tint); 168 | } 169 | .outline-variant { 170 | background-color: var(--md-sys-color-outline-variant); 171 | } 172 | .outline-variant-text { 173 | color: var(--md-sys-color-outline-variant); 174 | } 175 | .scrim { 176 | background-color: var(--md-sys-color-scrim); 177 | } 178 | .scrim-text { 179 | color: var(--md-sys-color-scrim); 180 | } 181 | -------------------------------------------------------------------------------- /css/styles.css: -------------------------------------------------------------------------------- 1 | @import url(theme.css); 2 | 3 | html { 4 | scroll-padding-top: 72px; 5 | } 6 | 7 | a { 8 | text-decoration: none; 9 | } 10 | 11 | a:hover { 12 | text-decoration: underline; 13 | } 14 | 15 | ul { 16 | margin: 0; 17 | padding: 0; 18 | list-style-type: none; 19 | } 20 | 21 | body { 22 | margin-top: 0; 23 | } 24 | 25 | header { 26 | margin: 0; 27 | width: 100%; 28 | position: fixed; 29 | } 30 | 31 | hr { 32 | text-align: center; 33 | margin-top: 0; 34 | margin-bottom: 0; 35 | } 36 | 37 | .top-bar { 38 | display: block; 39 | margin: 0; 40 | padding: 0; 41 | height: 64px; 42 | } 43 | 44 | .top-bar-left { 45 | display: flex; 46 | justify-content: flex-start; 47 | order: -1; 48 | flex: 1 1 auto; 49 | align-items: center; 50 | } 51 | 52 | .navigation-button { 53 | fill: var(--md-sys-color-on-background); 54 | display: grid; 55 | height: 48px; 56 | width: 48px; 57 | border-radius: 50%; 58 | place-items: center; 59 | } 60 | 61 | .navigation-button:hover { 62 | background-color: var(--md-sys-color-outline-variant); 63 | } 64 | 65 | .close-navigation-button { 66 | fill: var(--md-sys-color-on-surface); 67 | display: grid; 68 | height: 48px; 69 | width: 48px; 70 | margin-left: 8px; 71 | border-radius: 50%; 72 | place-items: center; 73 | } 74 | 75 | .close-navigation-button:hover { 76 | background-color: var(--md-sys-color-inverse-on-surface); 77 | } 78 | 79 | .top-bar-title { 80 | padding-left: 16px; 81 | } 82 | 83 | .sidebar { 84 | height: 100%; 85 | width: 0; 86 | position: fixed; 87 | z-index: 1; 88 | top: 0; 89 | left: 0; 90 | padding-top: 0; 91 | overflow-x: hidden; 92 | transition: 0.3s; 93 | } 94 | 95 | .overlay { 96 | position: fixed; 97 | z-index: 1; 98 | background-color: rgba(0, 0, 0, 0.8); 99 | } 100 | 101 | .navigation-list { 102 | display: block; 103 | } 104 | 105 | .navigation-item { 106 | margin: 8px 0; 107 | } 108 | 109 | .list-section { 110 | display: block; 111 | margin: 0 16px 8px 16px; 112 | padding: 24px; 113 | border-radius: 24px; 114 | } 115 | 116 | .top-title { 117 | width: 100%; 118 | margin: 0 auto; 119 | max-width: 1760px; 120 | } 121 | 122 | .count-container { 123 | border-radius: 24px; 124 | padding: 80px 24px 0 24px; 125 | margin: 0; 126 | display: grid; 127 | justify-content: space-between; 128 | grid-column-gap: 20px; 129 | column-gap: 20px; 130 | grid-template-columns: 6fr 1fr 6fr; 131 | user-select: none; 132 | } 133 | 134 | .item-number-container { 135 | border-radius: 24px; 136 | cursor: pointer; 137 | } 138 | 139 | .item-number-container:hover { 140 | color: var(--md-sys-color-on-primary-container); 141 | background-color: var(--md-sys-color-primary-container); 142 | } 143 | 144 | .count-title { 145 | margin: 0; 146 | text-align: center; 147 | } 148 | 149 | .count-number { 150 | margin: 16px 0 0 0; 151 | text-align: center; 152 | } 153 | 154 | .count-divider { 155 | width: 0; 156 | height: 100%; 157 | margin: 0 auto; 158 | } 159 | 160 | .title-brief { 161 | background-image: url(../images/titleBackground.png); 162 | background-position: 95% 5%; 163 | border-radius: 24px; 164 | padding: 56px; 165 | margin: 0; 166 | text-align: center; 167 | } 168 | 169 | 170 | 171 | .title { 172 | margin: 0 0 8px 0; 173 | font-weight: 475; 174 | font-size: 88px; 175 | } 176 | 177 | .brief { 178 | margin: 0; 179 | padding: 0; 180 | border: 0; 181 | } 182 | 183 | .github-homepage { 184 | margin: 24px 0 0 0; 185 | border: 0; 186 | border-radius: 48px; 187 | height: 60px; 188 | width: 160px; 189 | text-align: center; 190 | align-items: center; 191 | justify-content: center; 192 | cursor: pointer; 193 | } 194 | 195 | .github-homepage:hover { 196 | background-color: var(--md-sys-color-inverse-primary); 197 | } 198 | 199 | .overall-container { 200 | width: 100%; 201 | margin: 0 auto; 202 | max-width: 1200px; 203 | } 204 | 205 | .section-title { 206 | margin: 80px 24px 8px 24px; 207 | } 208 | 209 | .section-subtitle { 210 | margin: 24px 24px; 211 | } 212 | 213 | .background-vision-container { 214 | border-radius: 24px; 215 | padding: 24px; 216 | } 217 | 218 | .background-vision { 219 | line-height: 24px; 220 | margin: 0; 221 | } 222 | 223 | .lastupdate-container { 224 | border-radius: 24px; 225 | overflow: auto; 226 | padding: 8px 0 0 0; 227 | height: 360px; 228 | scrollbar-width: none; 229 | } 230 | 231 | .last-essay-container { 232 | border-radius: 24px; 233 | padding: 24px; 234 | margin: 0; 235 | display: grid; 236 | justify-content: space-between; 237 | grid-column-gap: 20px; 238 | column-gap: 20px; 239 | grid-template-columns: 3fr 1fr; 240 | } 241 | 242 | .last-essay-container:hover { 243 | color: var(--md-sys-color-on-primary-container); 244 | background-color: var(--md-sys-color-primary-container); 245 | } 246 | 247 | /*.lastupdate-container::-webkit-scrollbar { 248 | display: none; 249 | }*/ 250 | 251 | .edit-date { 252 | padding: 8px 24px; 253 | } 254 | 255 | .essay-container { 256 | border-radius: 24px; 257 | padding: 24px; 258 | margin: 0 0 8px 0; 259 | display: grid; 260 | justify-content: space-between; 261 | grid-column-gap: 20px; 262 | column-gap: 20px; 263 | grid-template-columns: 3fr 1fr; 264 | } 265 | 266 | .essay-container:hover { 267 | color: var(--md-sys-color-on-primary-container); 268 | background-color: var(--md-sys-color-primary-container); 269 | } 270 | 271 | .essay-content { 272 | margin: 0; 273 | padding: 0; 274 | } 275 | 276 | .essay-author { 277 | margin: 0; 278 | padding: 0; 279 | } 280 | 281 | .button-group { 282 | display: grid; 283 | grid-auto-flow: column; 284 | place-items: center; 285 | } 286 | 287 | .essay-button { 288 | display: grid; 289 | fill: var(--md-sys-color-on-surface-variant); 290 | height: 40px; 291 | width: 40px; 292 | border-radius: 50%; 293 | place-items: center; 294 | } 295 | 296 | .essay-button:hover { 297 | fill: var(--md-sys-color-on-primary); 298 | background-color: var(--md-sys-color-primary); 299 | } 300 | 301 | .placeholder { 302 | height: 40px; 303 | width: 40px; 304 | border-radius: 50%; 305 | } 306 | 307 | .media-coverage-content-container { 308 | border-radius: 24px; 309 | padding: 24px; 310 | margin: 0 0 8px 0; 311 | } 312 | 313 | .media-coverage-container { 314 | display: grid; 315 | justify-content: space-between; 316 | column-gap: 8px; 317 | grid-template-columns: 1fr 1fr; 318 | } 319 | 320 | .page-footer-container { 321 | display: flex; 322 | flex-direction: column; 323 | align-items: center; 324 | margin: 120px 8px 0; 325 | } 326 | 327 | .page-footer { 328 | padding: 64px 40px; 329 | } 330 | 331 | .about { 332 | display: grid; 333 | justify-content: space-between; 334 | max-width: 1200px; 335 | grid-column-gap: 20px; 336 | column-gap: 20px; 337 | grid-template-columns: 1fr 1fr; 338 | } 339 | 340 | .page-footer-title { 341 | margin: 0; 342 | } 343 | 344 | .cite { 345 | margin-top: 24px; 346 | margin-right: 64px; 347 | } 348 | 349 | .visitor-map { 350 | margin-top: 24px; 351 | margin-right: 64px; 352 | } 353 | 354 | .page-footer-instruction { 355 | margin: 0; 356 | line-height: 24px; 357 | } 358 | 359 | @media screen and (max-width: 857px) { 360 | .show-on-middle-screens { 361 | display: none; 362 | } 363 | .show-on-large-screens { 364 | display: none; 365 | } 366 | .title { 367 | margin: 0 0 8px 0; 368 | font-weight: 475; 369 | font-size: 45px 370 | } 371 | .page-footer-container{ 372 | display: block; 373 | width: auto; 374 | } 375 | .about { 376 | display: block; 377 | width: auto; 378 | } 379 | .media-coverage-container { 380 | display: block; 381 | } 382 | } 383 | 384 | @media screen and (min-width: 858px) and (max-width: 1294px) { 385 | .show-on-large-screens { 386 | display: none; 387 | } 388 | .show-on-small-screens { 389 | display: none; 390 | } 391 | } 392 | 393 | @media screen and (min-width: 1295px) { 394 | .show-on-middle-screens { 395 | display: none; 396 | } 397 | .show-on-small-screens { 398 | display: none; 399 | } 400 | } 401 | 402 | @media (prefers-color-scheme: dark) { 403 | .title-brief { 404 | filter: invert(100%); 405 | } 406 | 407 | .visitor-map { 408 | filter: invert(89%); 409 | } 410 | } -------------------------------------------------------------------------------- /css/typography.module.css: -------------------------------------------------------------------------------- 1 | .display-large{ 2 | font-family: var(--md-sys-typescale-display-large-font-family-name); 3 | font-style: var(--md-sys-typescale-display-large-font-family-style); 4 | font-weight: var(--md-sys-typescale-display-large-font-weight); 5 | font-size: var(--md-sys-typescale-display-large-font-size); 6 | letter-spacing: var(--md-sys-typescale-display-large-tracking); 7 | line-height: var(--md-sys-typescale-display-large-height); 8 | text-transform: var(--md-sys-typescale-display-large-text-transform); 9 | text-decoration: var(--md-sys-typescale-display-large-text-decoration); 10 | } 11 | .display-medium{ 12 | font-family: var(--md-sys-typescale-display-medium-font-family-name); 13 | font-style: var(--md-sys-typescale-display-medium-font-family-style); 14 | font-weight: var(--md-sys-typescale-display-medium-font-weight); 15 | font-size: var(--md-sys-typescale-display-medium-font-size); 16 | letter-spacing: var(--md-sys-typescale-display-medium-tracking); 17 | line-height: var(--md-sys-typescale-display-medium-height); 18 | text-transform: var(--md-sys-typescale-display-medium-text-transform); 19 | text-decoration: var(--md-sys-typescale-display-medium-text-decoration); 20 | } 21 | .display-small{ 22 | font-family: var(--md-sys-typescale-display-small-font-family-name); 23 | font-style: var(--md-sys-typescale-display-small-font-family-style); 24 | font-weight: var(--md-sys-typescale-display-small-font-weight); 25 | font-size: var(--md-sys-typescale-display-small-font-size); 26 | letter-spacing: var(--md-sys-typescale-display-small-tracking); 27 | line-height: var(--md-sys-typescale-display-small-height); 28 | text-transform: var(--md-sys-typescale-display-small-text-transform); 29 | text-decoration: var(--md-sys-typescale-display-small-text-decoration); 30 | } 31 | .headline-large{ 32 | font-family: var(--md-sys-typescale-headline-large-font-family-name); 33 | font-style: var(--md-sys-typescale-headline-large-font-family-style); 34 | font-weight: var(--md-sys-typescale-headline-large-font-weight); 35 | font-size: var(--md-sys-typescale-headline-large-font-size); 36 | letter-spacing: var(--md-sys-typescale-headline-large-tracking); 37 | line-height: var(--md-sys-typescale-headline-large-height); 38 | text-transform: var(--md-sys-typescale-headline-large-text-transform); 39 | text-decoration: var(--md-sys-typescale-headline-large-text-decoration); 40 | } 41 | .headline-medium{ 42 | font-family: var(--md-sys-typescale-headline-medium-font-family-name); 43 | font-style: var(--md-sys-typescale-headline-medium-font-family-style); 44 | font-weight: var(--md-sys-typescale-headline-medium-font-weight); 45 | font-size: var(--md-sys-typescale-headline-medium-font-size); 46 | letter-spacing: var(--md-sys-typescale-headline-medium-tracking); 47 | line-height: var(--md-sys-typescale-headline-medium-height); 48 | text-transform: var(--md-sys-typescale-headline-medium-text-transform); 49 | text-decoration: var(--md-sys-typescale-headline-medium-text-decoration); 50 | } 51 | .headline-small{ 52 | font-family: var(--md-sys-typescale-headline-small-font-family-name); 53 | font-style: var(--md-sys-typescale-headline-small-font-family-style); 54 | font-weight: var(--md-sys-typescale-headline-small-font-weight); 55 | font-size: var(--md-sys-typescale-headline-small-font-size); 56 | letter-spacing: var(--md-sys-typescale-headline-small-tracking); 57 | line-height: var(--md-sys-typescale-headline-small-height); 58 | text-transform: var(--md-sys-typescale-headline-small-text-transform); 59 | text-decoration: var(--md-sys-typescale-headline-small-text-decoration); 60 | } 61 | .body-large{ 62 | font-family: var(--md-sys-typescale-body-large-font-family-name); 63 | font-style: var(--md-sys-typescale-body-large-font-family-style); 64 | font-weight: var(--md-sys-typescale-body-large-font-weight); 65 | font-size: var(--md-sys-typescale-body-large-font-size); 66 | letter-spacing: var(--md-sys-typescale-body-large-tracking); 67 | line-height: var(--md-sys-typescale-body-large-height); 68 | text-transform: var(--md-sys-typescale-body-large-text-transform); 69 | text-decoration: var(--md-sys-typescale-body-large-text-decoration); 70 | } 71 | .body-medium{ 72 | font-family: var(--md-sys-typescale-body-medium-font-family-name); 73 | font-style: var(--md-sys-typescale-body-medium-font-family-style); 74 | font-weight: var(--md-sys-typescale-body-medium-font-weight); 75 | font-size: var(--md-sys-typescale-body-medium-font-size); 76 | letter-spacing: var(--md-sys-typescale-body-medium-tracking); 77 | line-height: var(--md-sys-typescale-body-medium-height); 78 | text-transform: var(--md-sys-typescale-body-medium-text-transform); 79 | text-decoration: var(--md-sys-typescale-body-medium-text-decoration); 80 | } 81 | .body-small{ 82 | font-family: var(--md-sys-typescale-body-small-font-family-name); 83 | font-style: var(--md-sys-typescale-body-small-font-family-style); 84 | font-weight: var(--md-sys-typescale-body-small-font-weight); 85 | font-size: var(--md-sys-typescale-body-small-font-size); 86 | letter-spacing: var(--md-sys-typescale-body-small-tracking); 87 | line-height: var(--md-sys-typescale-body-small-height); 88 | text-transform: var(--md-sys-typescale-body-small-text-transform); 89 | text-decoration: var(--md-sys-typescale-body-small-text-decoration); 90 | } 91 | .label-large{ 92 | font-family: var(--md-sys-typescale-label-large-font-family-name); 93 | font-style: var(--md-sys-typescale-label-large-font-family-style); 94 | font-weight: var(--md-sys-typescale-label-large-font-weight); 95 | font-size: var(--md-sys-typescale-label-large-font-size); 96 | letter-spacing: var(--md-sys-typescale-label-large-tracking); 97 | line-height: var(--md-sys-typescale-label-large-height); 98 | text-transform: var(--md-sys-typescale-label-large-text-transform); 99 | text-decoration: var(--md-sys-typescale-label-large-text-decoration); 100 | } 101 | .label-medium{ 102 | font-family: var(--md-sys-typescale-label-medium-font-family-name); 103 | font-style: var(--md-sys-typescale-label-medium-font-family-style); 104 | font-weight: var(--md-sys-typescale-label-medium-font-weight); 105 | font-size: var(--md-sys-typescale-label-medium-font-size); 106 | letter-spacing: var(--md-sys-typescale-label-medium-tracking); 107 | line-height: var(--md-sys-typescale-label-medium-height); 108 | text-transform: var(--md-sys-typescale-label-medium-text-transform); 109 | text-decoration: var(--md-sys-typescale-label-medium-text-decoration); 110 | } 111 | .label-small{ 112 | font-family: var(--md-sys-typescale-label-small-font-family-name); 113 | font-style: var(--md-sys-typescale-label-small-font-family-style); 114 | font-weight: var(--md-sys-typescale-label-small-font-weight); 115 | font-size: var(--md-sys-typescale-label-small-font-size); 116 | letter-spacing: var(--md-sys-typescale-label-small-tracking); 117 | line-height: var(--md-sys-typescale-label-small-height); 118 | text-transform: var(--md-sys-typescale-label-small-text-transform); 119 | text-decoration: var(--md-sys-typescale-label-small-text-decoration); 120 | } 121 | .title-large{ 122 | font-family: var(--md-sys-typescale-title-large-font-family-name); 123 | font-style: var(--md-sys-typescale-title-large-font-family-style); 124 | font-weight: var(--md-sys-typescale-title-large-font-weight); 125 | font-size: var(--md-sys-typescale-title-large-font-size); 126 | letter-spacing: var(--md-sys-typescale-title-large-tracking); 127 | line-height: var(--md-sys-typescale-title-large-height); 128 | text-transform: var(--md-sys-typescale-title-large-text-transform); 129 | text-decoration: var(--md-sys-typescale-title-large-text-decoration); 130 | } 131 | .title-medium{ 132 | font-family: var(--md-sys-typescale-title-medium-font-family-name); 133 | font-style: var(--md-sys-typescale-title-medium-font-family-style); 134 | font-weight: var(--md-sys-typescale-title-medium-font-weight); 135 | font-size: var(--md-sys-typescale-title-medium-font-size); 136 | letter-spacing: var(--md-sys-typescale-title-medium-tracking); 137 | line-height: var(--md-sys-typescale-title-medium-height); 138 | text-transform: var(--md-sys-typescale-title-medium-text-transform); 139 | text-decoration: var(--md-sys-typescale-title-medium-text-decoration); 140 | } 141 | .title-small{ 142 | font-family: var(--md-sys-typescale-title-small-font-family-name); 143 | font-style: var(--md-sys-typescale-title-small-font-family-style); 144 | font-weight: var(--md-sys-typescale-title-small-font-weight); 145 | font-size: var(--md-sys-typescale-title-small-font-size); 146 | letter-spacing: var(--md-sys-typescale-title-small-tracking); 147 | line-height: var(--md-sys-typescale-title-small-height); 148 | text-transform: var(--md-sys-typescale-title-small-text-transform); 149 | text-decoration: var(--md-sys-typescale-title-small-text-decoration); 150 | } 151 | -------------------------------------------------------------------------------- /css/tokens.css: -------------------------------------------------------------------------------- 1 | :root { 2 | --md-source: #97cfea; 3 | /* primary */ 4 | --md-ref-palette-primary0: #000000; 5 | --md-ref-palette-primary10: #001f2a; 6 | --md-ref-palette-primary20: #003546; 7 | --md-ref-palette-primary25: #004155; 8 | --md-ref-palette-primary30: #004d64; 9 | --md-ref-palette-primary35: #005a74; 10 | --md-ref-palette-primary40: #006684; 11 | --md-ref-palette-primary50: #0081a5; 12 | --md-ref-palette-primary60: #0b9cc7; 13 | --md-ref-palette-primary70: #41b7e4; 14 | --md-ref-palette-primary80: #68d3ff; 15 | --md-ref-palette-primary90: #bee9ff; 16 | --md-ref-palette-primary95: #e0f4ff; 17 | --md-ref-palette-primary98: #f4faff; 18 | --md-ref-palette-primary99: #fafcff; 19 | --md-ref-palette-primary100: #ffffff; 20 | /* secondary */ 21 | --md-ref-palette-secondary0: #000000; 22 | --md-ref-palette-secondary10: #081e27; 23 | --md-ref-palette-secondary20: #1f333c; 24 | --md-ref-palette-secondary25: #2a3e48; 25 | --md-ref-palette-secondary30: #354a53; 26 | --md-ref-palette-secondary35: #41555f; 27 | --md-ref-palette-secondary40: #4d616c; 28 | --md-ref-palette-secondary50: #657a85; 29 | --md-ref-palette-secondary60: #7f949f; 30 | --md-ref-palette-secondary70: #99aeba; 31 | --md-ref-palette-secondary80: #b4cad6; 32 | --md-ref-palette-secondary90: #d0e6f2; 33 | --md-ref-palette-secondary95: #e0f4ff; 34 | --md-ref-palette-secondary98: #f4faff; 35 | --md-ref-palette-secondary99: #fafcff; 36 | --md-ref-palette-secondary100: #ffffff; 37 | /* tertiary */ 38 | --md-ref-palette-tertiary0: #000000; 39 | --md-ref-palette-tertiary10: #1a1836; 40 | --md-ref-palette-tertiary20: #2f2d4d; 41 | --md-ref-palette-tertiary25: #3a3858; 42 | --md-ref-palette-tertiary30: #454364; 43 | --md-ref-palette-tertiary35: #514f71; 44 | --md-ref-palette-tertiary40: #5d5b7d; 45 | --md-ref-palette-tertiary50: #767397; 46 | --md-ref-palette-tertiary60: #908db2; 47 | --md-ref-palette-tertiary70: #aba7ce; 48 | --md-ref-palette-tertiary80: #c6c2ea; 49 | --md-ref-palette-tertiary90: #e3dfff; 50 | --md-ref-palette-tertiary95: #f3eeff; 51 | --md-ref-palette-tertiary98: #fcf8ff; 52 | --md-ref-palette-tertiary99: #fffbff; 53 | --md-ref-palette-tertiary100: #ffffff; 54 | /* neutral */ 55 | --md-ref-palette-neutral0: #000000; 56 | --md-ref-palette-neutral10: #191c1e; 57 | --md-ref-palette-neutral20: #2e3132; 58 | --md-ref-palette-neutral25: #393c3e; 59 | --md-ref-palette-neutral30: #444749; 60 | --md-ref-palette-neutral35: #505355; 61 | --md-ref-palette-neutral40: #5c5f61; 62 | --md-ref-palette-neutral50: #757779; 63 | --md-ref-palette-neutral60: #8f9193; 64 | --md-ref-palette-neutral70: #a9abad; 65 | --md-ref-palette-neutral80: #c5c7c9; 66 | --md-ref-palette-neutral90: #e1e2e4; 67 | --md-ref-palette-neutral95: #eff1f3; 68 | --md-ref-palette-neutral98: #f8f9fb; 69 | --md-ref-palette-neutral99: #fbfcfe; 70 | --md-ref-palette-neutral100: #ffffff; 71 | /* neutral-variant */ 72 | --md-ref-palette-neutral-variant0: #000000; 73 | --md-ref-palette-neutral-variant10: #151d20; 74 | --md-ref-palette-neutral-variant20: #2a3136; 75 | --md-ref-palette-neutral-variant25: #353d41; 76 | --md-ref-palette-neutral-variant30: #40484c; 77 | --md-ref-palette-neutral-variant35: #4c5458; 78 | --md-ref-palette-neutral-variant40: #585f64; 79 | --md-ref-palette-neutral-variant50: #70787d; 80 | --md-ref-palette-neutral-variant60: #8a9297; 81 | --md-ref-palette-neutral-variant70: #a5acb1; 82 | --md-ref-palette-neutral-variant80: #c0c8cd; 83 | --md-ref-palette-neutral-variant90: #dce4e9; 84 | --md-ref-palette-neutral-variant95: #eaf2f7; 85 | --md-ref-palette-neutral-variant98: #f4faff; 86 | --md-ref-palette-neutral-variant99: #fafcff; 87 | --md-ref-palette-neutral-variant100: #ffffff; 88 | /* error */ 89 | --md-ref-palette-error0: #000000; 90 | --md-ref-palette-error10: #410002; 91 | --md-ref-palette-error20: #690005; 92 | --md-ref-palette-error25: #7e0007; 93 | --md-ref-palette-error30: #93000a; 94 | --md-ref-palette-error35: #a80710; 95 | --md-ref-palette-error40: #ba1a1a; 96 | --md-ref-palette-error50: #de3730; 97 | --md-ref-palette-error60: #ff5449; 98 | --md-ref-palette-error70: #ff897d; 99 | --md-ref-palette-error80: #ffb4ab; 100 | --md-ref-palette-error90: #ffdad6; 101 | --md-ref-palette-error95: #ffedea; 102 | --md-ref-palette-error98: #fff8f7; 103 | --md-ref-palette-error99: #fffbff; 104 | --md-ref-palette-error100: #ffffff; 105 | /* light */ 106 | --md-sys-color-primary-light: #006684; 107 | --md-sys-color-on-primary-light: #ffffff; 108 | --md-sys-color-primary-container-light: #bee9ff; 109 | --md-sys-color-on-primary-container-light: #001f2a; 110 | --md-sys-color-secondary-light: #4d616c; 111 | --md-sys-color-on-secondary-light: #ffffff; 112 | --md-sys-color-secondary-container-light: #d0e6f2; 113 | --md-sys-color-on-secondary-container-light: #081e27; 114 | --md-sys-color-tertiary-light: #5d5b7d; 115 | --md-sys-color-on-tertiary-light: #ffffff; 116 | --md-sys-color-tertiary-container-light: #e3dfff; 117 | --md-sys-color-on-tertiary-container-light: #1a1836; 118 | --md-sys-color-error-light: #ba1a1a; 119 | --md-sys-color-error-container-light: #ffdad6; 120 | --md-sys-color-on-error-light: #ffffff; 121 | --md-sys-color-on-error-container-light: #410002; 122 | --md-sys-color-background-light: #fbfcfe; 123 | --md-sys-color-on-background-light: #191c1e; 124 | --md-sys-color-surface-light: #fbfcfe; 125 | --md-sys-color-on-surface-light: #191c1e; 126 | --md-sys-color-surface-variant-light: #dce4e9; 127 | --md-sys-color-on-surface-variant-light: #40484c; 128 | --md-sys-color-outline-light: #70787d; 129 | --md-sys-color-inverse-on-surface-light: #eff1f3; 130 | --md-sys-color-inverse-surface-light: #2e3132; 131 | --md-sys-color-inverse-primary-light: #68d3ff; 132 | --md-sys-color-shadow-light: #000000; 133 | --md-sys-color-surface-tint-light: #006684; 134 | --md-sys-color-outline-variant-light: #c0c8cd; 135 | --md-sys-color-scrim-light: #000000; 136 | /* dark */ 137 | --md-sys-color-primary-dark: #68d3ff; 138 | --md-sys-color-on-primary-dark: #003546; 139 | --md-sys-color-primary-container-dark: #004d64; 140 | --md-sys-color-on-primary-container-dark: #bee9ff; 141 | --md-sys-color-secondary-dark: #b4cad6; 142 | --md-sys-color-on-secondary-dark: #1f333c; 143 | --md-sys-color-secondary-container-dark: #354a53; 144 | --md-sys-color-on-secondary-container-dark: #d0e6f2; 145 | --md-sys-color-tertiary-dark: #c6c2ea; 146 | --md-sys-color-on-tertiary-dark: #2f2d4d; 147 | --md-sys-color-tertiary-container-dark: #454364; 148 | --md-sys-color-on-tertiary-container-dark: #e3dfff; 149 | --md-sys-color-error-dark: #ffb4ab; 150 | --md-sys-color-error-container-dark: #93000a; 151 | --md-sys-color-on-error-dark: #690005; 152 | --md-sys-color-on-error-container-dark: #ffdad6; 153 | --md-sys-color-background-dark: #191c1e; 154 | --md-sys-color-on-background-dark: #e1e2e4; 155 | --md-sys-color-surface-dark: #191c1e; 156 | --md-sys-color-on-surface-dark: #e1e2e4; 157 | --md-sys-color-surface-variant-dark: #40484c; 158 | --md-sys-color-on-surface-variant-dark: #c0c8cd; 159 | --md-sys-color-outline-dark: #8a9297; 160 | --md-sys-color-inverse-on-surface-dark: #191c1e; 161 | --md-sys-color-inverse-surface-dark: #e1e2e4; 162 | --md-sys-color-inverse-primary-dark: #006684; 163 | --md-sys-color-shadow-dark: #000000; 164 | --md-sys-color-surface-tint-dark: #68d3ff; 165 | --md-sys-color-outline-variant-dark: #40484c; 166 | --md-sys-color-scrim-dark: #000000; 167 | /* display - large */ 168 | --md-sys-typescale-display-large-font-family-name: Outfit; 169 | --md-sys-typescale-display-large-font-family-style: Regular; 170 | --md-sys-typescale-display-large-font-weight: 475; 171 | --md-sys-typescale-display-large-font-size: 57px; 172 | --md-sys-typescale-display-large-line-height: 64px; 173 | --md-sys-typescale-display-large-letter-spacing: -0.25px; 174 | /* display - medium */ 175 | --md-sys-typescale-display-medium-font-family-name: Outfit; 176 | --md-sys-typescale-display-medium-font-family-style: Regular; 177 | --md-sys-typescale-display-medium-font-weight: 475; 178 | --md-sys-typescale-display-medium-font-size: 45px; 179 | --md-sys-typescale-display-medium-line-height: 52px; 180 | --md-sys-typescale-display-medium-letter-spacing: 0px; 181 | /* display - small */ 182 | --md-sys-typescale-display-small-font-family-name: Outfit; 183 | --md-sys-typescale-display-small-font-family-style: Regular; 184 | --md-sys-typescale-display-small-font-weight: 475; 185 | --md-sys-typescale-display-small-font-size: 36px; 186 | --md-sys-typescale-display-small-line-height: 44px; 187 | --md-sys-typescale-display-small-letter-spacing: 0px; 188 | /* headline - large */ 189 | --md-sys-typescale-headline-large-font-family-name: Outfit; 190 | --md-sys-typescale-headline-large-font-family-style: Regular; 191 | --md-sys-typescale-headline-large-font-weight: 400px; 192 | --md-sys-typescale-headline-large-font-size: 32px; 193 | --md-sys-typescale-headline-large-line-height: 40px; 194 | --md-sys-typescale-headline-large-letter-spacing: 0px; 195 | /* headline - medium */ 196 | --md-sys-typescale-headline-medium-font-family-name: Outfit; 197 | --md-sys-typescale-headline-medium-font-family-style: Regular; 198 | --md-sys-typescale-headline-medium-font-weight: 400px; 199 | --md-sys-typescale-headline-medium-font-size: 28px; 200 | --md-sys-typescale-headline-medium-line-height: 36px; 201 | --md-sys-typescale-headline-medium-letter-spacing: 0px; 202 | /* headline - small */ 203 | --md-sys-typescale-headline-small-font-family-name: Outfit; 204 | --md-sys-typescale-headline-small-font-family-style: Regular; 205 | --md-sys-typescale-headline-small-font-weight: 400px; 206 | --md-sys-typescale-headline-small-font-size: 24px; 207 | --md-sys-typescale-headline-small-line-height: 32px; 208 | --md-sys-typescale-headline-small-letter-spacing: 0px; 209 | /* body - large */ 210 | --md-sys-typescale-body-large-font-family-name: Outfit; 211 | --md-sys-typescale-body-large-font-family-style: Regular; 212 | --md-sys-typescale-body-large-font-weight: 400px; 213 | --md-sys-typescale-body-large-font-size: 16px; 214 | --md-sys-typescale-body-large-line-height: 24px; 215 | --md-sys-typescale-body-large-letter-spacing: 0.50px; 216 | /* body - medium */ 217 | --md-sys-typescale-body-medium-font-family-name: Outfit; 218 | --md-sys-typescale-body-medium-font-family-style: Regular; 219 | --md-sys-typescale-body-medium-font-weight: 400px; 220 | --md-sys-typescale-body-medium-font-size: 14px; 221 | --md-sys-typescale-body-medium-line-height: 20px; 222 | --md-sys-typescale-body-medium-letter-spacing: 0.25px; 223 | /* body - small */ 224 | --md-sys-typescale-body-small-font-family-name: Outfit; 225 | --md-sys-typescale-body-small-font-family-style: Regular; 226 | --md-sys-typescale-body-small-font-weight: 400px; 227 | --md-sys-typescale-body-small-font-size: 12px; 228 | --md-sys-typescale-body-small-line-height: 16px; 229 | --md-sys-typescale-body-small-letter-spacing: 0.40px; 230 | /* label - large */ 231 | --md-sys-typescale-label-large-font-family-name: Outfit; 232 | --md-sys-typescale-label-large-font-family-style: Medium; 233 | --md-sys-typescale-label-large-font-weight: 500px; 234 | --md-sys-typescale-label-large-font-size: 14px; 235 | --md-sys-typescale-label-large-line-height: 20px; 236 | --md-sys-typescale-label-large-letter-spacing: 0.10px; 237 | /* label - medium */ 238 | --md-sys-typescale-label-medium-font-family-name: Outfit; 239 | --md-sys-typescale-label-medium-font-family-style: Medium; 240 | --md-sys-typescale-label-medium-font-weight: 500px; 241 | --md-sys-typescale-label-medium-font-size: 12px; 242 | --md-sys-typescale-label-medium-line-height: 16px; 243 | --md-sys-typescale-label-medium-letter-spacing: 0.50px; 244 | /* label - small */ 245 | --md-sys-typescale-label-small-font-family-name: Outfit; 246 | --md-sys-typescale-label-small-font-family-style: Medium; 247 | --md-sys-typescale-label-small-font-weight: 500px; 248 | --md-sys-typescale-label-small-font-size: 11px; 249 | --md-sys-typescale-label-small-line-height: 16px; 250 | --md-sys-typescale-label-small-letter-spacing: 0.50px; 251 | /* title - large */ 252 | --md-sys-typescale-title-large-font-family-name: Outfit; 253 | --md-sys-typescale-title-large-font-family-style: Regular; 254 | --md-sys-typescale-title-large-font-weight: 400px; 255 | --md-sys-typescale-title-large-font-size: 22px; 256 | --md-sys-typescale-title-large-line-height: 28px; 257 | --md-sys-typescale-title-large-letter-spacing: 0px; 258 | /* title - medium */ 259 | --md-sys-typescale-title-medium-font-family-name: Outfit; 260 | --md-sys-typescale-title-medium-font-family-style: Medium; 261 | --md-sys-typescale-title-medium-font-weight: 500px; 262 | --md-sys-typescale-title-medium-font-size: 16px; 263 | --md-sys-typescale-title-medium-line-height: 24px; 264 | --md-sys-typescale-title-medium-letter-spacing: 0.15px; 265 | /* title - small */ 266 | --md-sys-typescale-title-small-font-family-name: Outfit; 267 | --md-sys-typescale-title-small-font-family-style: Medium; 268 | --md-sys-typescale-title-small-font-weight: 500px; 269 | --md-sys-typescale-title-small-font-size: 14px; 270 | --md-sys-typescale-title-small-line-height: 20px; 271 | --md-sys-typescale-title-small-letter-spacing: 0.10px; 272 | } 273 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Advances in Partial/Complementary Label Learning 2 | 3 | [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome) 4 | Contrib PaperNum ![Stars](https://img.shields.io/github/stars/wu-dd/Advances-in-Partial-and-Complementary-Label-Learning?color=yellow&label=Stars) ![Forks](https://img.shields.io/github/forks/wu-dd/Advances-in-Partial-and-Complementary-Label-Learning?color=green&label=Forks) 5 | 6 | 7 | 8 | 9 | 10 | **Advances in Partial/Complementary Label Learning** provides the most advanced and detailed information on partial/complementary label learning field. 11 | 12 | **Partial/complementary label learning** is an emerging framework in weakly supervised machine learning with broad application prospects. It handles the case in which each training example corresponds to a candidate label set and only one label concealed in the set is the ground-truth label. 13 | 14 | **This project is curated and maintained by [Dong-Dong Wu](https://wu-dd.github.io/)**. I will do my best to keep the project up to date. If you have any suggestions or are interested in being contributors, feel free to drop me an email. 15 | 16 | 17 | 18 | #### [How to submit a pull request?](./CONTRIBUTING.md) 19 | 20 | + :globe_with_meridians: Project Page 21 | + :octocat: Code 22 | + :book: `bibtex` 23 | 24 | ## Latest Updates 25 | 26 | + [2025/09/22] Update some papers published on conferences. If you find some errors, please feel free to contact me! 27 | + [2024/12/08] Update some code links of papers. 28 | + [2024/09/13] A major overhaul of the original github repository. 29 | 30 | ## Contents 31 | 32 | - [Main](#main) 33 | - [Survey](#survey) 34 | - [benchmark](#benchmark) 35 | - [Generative Modeling](#theory-based) 36 | - [Understanding](#understanding) 37 | - [Better Optimization](#better-optimization) 38 | - [Partial Multi-Label Learning](#partial-multi-label-learning) 39 | - [Noisy Partial Label Learning](#noisy-partial-label-learning) 40 | - [Semi-Supervised Partial Label Learning](#semi-supervised-partial-label-learning) 41 | - [Multi-Instance Partial Label Learning](#multi-instance-learning) 42 | - [Imbalanced Partial Label Problem](#class-imbalance-problem) 43 | - [Out-of-distributrion Partial Label Learning](#partial-label-regression) 44 | - [Federated Partial Label Learning](#fed-pll>) 45 | - [Partial Label Regression](#partial-label-regression) 46 | - [Dimensionality Reduction](#dimensionality-reduction) 47 | - [Multi-Complementary Label Learning](#multi-complementary-label-learning) 48 | - [Multi-View Learning](#multi-view-learning) 49 | - [Adversarial Training](#adversarial-training) 50 | - [Negative Learning](#negative-learning) 51 | - [Incremental Learning](#incremental-learning) 52 | - [Online Learning](#online-learning) 53 | - [Conformal Prediction](#conformal-prediction) 54 | - [Few-Shot Learning](#few-shot-learning) 55 | - [Open-Set Problem](#open-set-problem) 56 | - [Data Augmentation](#data-augmentation) 57 | - [Multi-Dimensional](#multi-dimensional) 58 | - [Domain Adaptation](#domain-adaptation) 59 | - [Applications](#applications) 60 | - [Audio](#audio) 61 | - [Text](#text) 62 | - [Sequence](#sequence-classification) 63 | - [Recognition](#recognition) 64 | - [Object Localization](#object-localization) 65 | - [Map Reconstruction](#map-reconstruction) 66 | - [Semi-Supervised Learning](#semi-supervised-learning) 67 | - [Active Learning](#active-learning) 68 | - [Noisy Label Learning](#noisy-label-learning) 69 | - [Test-Time Adaptation](#TTA) 70 | 71 | 72 | 73 | ## Main 74 | 75 | - [Learning from Complementary Labels](https://arxiv.org/abs/1705.07541) (NeurIPS 2017) [:octocat:](https://github.com/takashiishida/comp) 76 | - [Learning from Partial Labels](https://jmlr.org/papers/v12/cour11a.html) (JMLR 2011) 77 | 78 | 79 | 80 | ### Survey 81 | 82 | - [Partial label learning: Taxonomy, analysis and outlook](https://www.sciencedirect.com/science/article/abs/pii/S0893608023000825) (NN 2023) 83 | 84 | 85 | 86 | ### Benchmark 87 | 88 | - [CLImage: Human-Annotated Datasets for Complementary-Label Learning](https://arxiv.org/abs/2305.08295) (TMLR 2025) 89 | - [Realistic Evaluation of Deep Partial-Label Learning Algorithms](https://arxiv.org/pdf/2502.10184) (ICLR 2025) 90 | 91 | 92 | 93 | ### Modeling 94 | 95 | - [Learning with Biased Complementary Labels](https://arxiv.org/abs/1711.09535) (ECCV 2018) [:octocat:](https://github.com/takashiishida/comp) 96 | - [Complementary-Label Learning for Arbitrary Losses and Models](https://arxiv.org/abs/1810.04327) (ICML 2019) [:octocat:](https://github.com/takashiishida/comp) 97 | - [Unbiased Risk Estimators Can Mislead: A Case Study of Learning with Complementary Labels](https://arxiv.org/abs/2007.02235) (ICML 2020) 98 | - [Provably Consistent Partial-Label Learning](https://arxiv.org/abs/2007.08929) (NeurIPS 2020) [:octocat:](https://lfeng1995.github.io/Code/RCCC.zip) 99 | - [Leveraged Weighted Loss for Partial Label Learning](https://arxiv.org/abs/2106.05731) (ICML 2021) [:octocat:](https://github.com/hongwei-wen/LW-loss-for-partial-label) 100 | - [Unbiased Risk Estimator to Multi-Labeled Complementary Label Learning](https://www.ijcai.org/proceedings/2023/415) (IJCAI 2023) [:octocat:](https://github.com/GaoYi439/GDF) 101 | - [Learning with Complementary Labels Revisited: The Selected-Completely-at-Random Setting Is More Practical](https://arxiv.org/abs/2311.15502) (ICML 2024) [:octocat:](https://github.com/wwangwitsel/SCARCE) 102 | - [Towards Unbiased Exploration in Partial Label Learning](https://arxiv.org/abs/2307.00465) (JMLR 2024) 103 | 104 | 105 | 106 | ### Understanding 107 | 108 | - [Bridging Ordinary-Label Learning and Complementary-Label Learning](https://proceedings.mlr.press/v129/katsura20a.html) (ACML 2020) 109 | - [On the Power of Deep but Naive Partial Label Learning](https://arxiv.org/abs/2010.11600) (ICASSP 2021) [:octocat:](https://github.com/mikigom/DNPL-PyTorch) 110 | - [Learning from a Complementary-label Source Domain: Theory and Algorithms](https://arxiv.org/abs/2008.01454) (TNNLS 2021) 111 | - [A Unifying Probabilistic Framework for Partially Labeled Data Learning](https://ieeexplore.ieee.org/document/9983986) (TPAMI 2023) 112 | - [Candidate Label Set Pruning: A Data-centric Perspective for Deep Partial-label Learning](https://openreview.net/forum?id=Fk5IzauJ7F) (ICLR 2024) 113 | - [Understanding Self-Distillation and Partial Label Learning in Multi-Class Classification with Label Noise](https://arxiv.org/abs/2402.10482) (2024) 114 | - [What Makes Partial-Label Learning Algorithms Effective?](https://openreview.net/forum?id=JpqEzPTuv6) (NeurIPS 2024) 115 | 116 | 117 | 118 | ### Better Optimization 119 | 120 | - [A Conditional Multinomial Mixture Model for Superset Label Learning](https://papers.nips.cc/paper_files/paper/2012/hash/aaebdb8bb6b0e73f6c3c54a0ab0c6415-Abstract.html) (NeurIPS 2012) 121 | - [GM-PLL: Graph Matching based Partial Label Learning](https://arxiv.org/pdf/1901.03073) (TKDE 2019) 122 | - [Partial Label Learning via Label Enhancement](https://ojs.aaai.org/index.php/AAAI/article/view/4497) (AAAI 2019) 123 | - [Partial Label Learning with Self-Guided Retraining](https://arxiv.org/abs/1902.03045) (AAAI 2019) 124 | - [Partial Label Learning by Semantic Difference Maximization](https://www.ijcai.org/proceedings/2019/318) (IJCAI 2019) 125 | - [Partial Label Learning with Unlabeled Data](https://www.ijcai.org/proceedings/2019/521) (IJCAI 2019) 126 | - [Adaptive Graph Guided Disambiguation for Partial Label Learning](https://dl.acm.org/doi/10.1145/3292500.3330840) (KDD 2019) 127 | - [A Self-Paced Regularization Framework for Partial-Label Learning](https://ieeexplore.ieee.org/document/9094702) (TYCB 2020) 128 | - [Large Margin Partial Label Machine](https://ieeexplore.ieee.org/document/8826247) (TNNLS 2020) 129 | - [Learning with Noisy Partial Labels by Simultaneously Leveraging Global and Local Consistencies](https://dl.acm.org/doi/10.1145/3340531.3411885) (CIKM 2020) 130 | - [Network Cooperation with Progressive Disambiguation for Partial Label Learning](https://arxiv.org/abs/2002.11919) (ECML-PKDD 2020) 131 | - [Deep Discriminative CNN with Temporal Ensembling for Ambiguously-Labeled Image Classification](https://ojs.aaai.org/index.php/AAAI/article/view/6959) (AAAI 2020) 132 | - [Generative-Discriminative Complementary Learning](https://arxiv.org/abs/1904.01612) (AAAI 2020) 133 | - [Partial Label Learning with Batch Label Correction](https://ojs.aaai.org/index.php/AAAI/article/view/6132) (AAAI 2020) 134 | - [Progressive Identification of True Labels for Partial-Label Learning](https://arxiv.org/abs/2002.08053) (ICML 2020) 135 | - [Generalized Large Margin -NN for Partial Label Learning](https://ieeexplore.ieee.org/document/9529072) (TMM2021) 136 | - [Adaptive Graph Guided Disambiguation for Partial Label Learning](https://ieeexplore.ieee.org/document/9573413) (TPAMI 2022) 137 | - [Discriminative Metric Learning for Partial Label Learning](https://ieeexplore.ieee.org/document/9585342) (TNNLS 2021) 138 | - [Top-k Partial Label Machine](https://ieeexplore.ieee.org/document/9447152) (TNNLS 2021) 139 | - [Detecting the Fake Candidate Instances: Ambiguous Label Learning with Generative Adversarial Networks](https://dl.acm.org/doi/abs/10.1145/3459637.3482251) (CIKM 2021) 140 | - [Discriminative Complementary-Label Learning with Weighted Loss](https://proceedings.mlr.press/v139/gao21d.html) (ICML 2021) 141 | - [Instance-Dependent Partial Label Learning](https://arxiv.org/abs/2110.12911) (NeurIPS 2021) 142 | - [A Generative Model for Partial Label Learning](https://ieeexplore.ieee.org/abstract/document/9428103) (ICME 2021) 143 | - [Learning with Proper Partial Labels](https://arxiv.org/abs/2112.12303) (NearoComputing 2022) 144 | - [Biased Complementary-Label Learning Without True Labels](https://ieeexplore.ieee.org/document/9836971) (TNNLS 2022) 145 | - [Exploiting Class Activation Value for Partial-Label Learning](https://openreview.net/forum?id=qqdXHUGec9h) (ICLR 2022) [:octocat:](https://github.com/Ferenas/CAVL) 146 | - [PiCO: Contrastive Label Disambiguation for Partial Label Learning](https://openreview.net/forum?id=EhYjZy6e1gJ) (ICLR 2022) 147 | - [Deep Graph Matching for Partial Label Learning](https://www.ijcai.org/proceedings/2022/459) (IJCAI 2022) 148 | - [Exploring Binary Classification Hidden within Partial Label Learning](https://www.ijcai.org/proceedings/2022/456) (IJCAI 2022) 149 | - [Ambiguity-Induced Contrastive Learning for Instance-Dependent Partial Label Learning](https://www.ijcai.org/proceedings/2022/502) (IJCAI 2022) 150 | - [Partial Label Learning via Label Influence Function](https://proceedings.mlr.press/v162/gong22c.html) (ICML 2022) 151 | - [Revisiting Consistency Regularization for Deep Partial Label Learning](https://proceedings.mlr.press/v162/wu22l.html) (ICML 2022) 152 | - [Partial Label Learning with Semantic Label Representations ](https://dl.acm.org/doi/abs/10.1145/3534678.3539434) (KDD 2022) 153 | - [Progressive Purification for Instance-Dependent Partial Label Learning](https://arxiv.org/abs/2206.00830) (ICML 2023) [:octocat:](https://github.com/palm-ml/POP) 154 | - [GraphDPI: Partial label disambiguation by graph representation learning via mutual information maximization](https://www.sciencedirect.com/science/article/abs/pii/S0031320322006136) (PR 2023) 155 | - [Variational Label Enhancement](https://ieeexplore.ieee.org/document/9875104) (TPAMI 2023) 156 | - [CMW-Net: Learning a Class-Aware Sample Weighting Mapping for Robust Deep Learning](https://arxiv.org/abs/2202.05613) (TPAMI 2023) 157 | - [Reduction from Complementary-Label Learning to Probability Estimates](https://arxiv.org/abs/2209.09500) (PAKDD 2023) 158 | - [Decompositional Generation Process for Instance-Dependent Partial Label Learning](https://arxiv.org/abs/2204.03845) (ICLR 2023) 159 | - [Mutual Partial Label Learning with Competitive Label Noise](https://openreview.net/forum?id=EUrxG8IBCrC) (ICLR 2023) 160 | - [Can Label-Specific Features Help Partial-Label Learning? ](https://ojs.aaai.org/index.php/AAAI/article/view/25904) (AAAI 2023) 161 | - [Learning with Partial Labels from Semi-supervised Perspective](https://arxiv.org/abs/2211.13655) (AAAI 2023) 162 | - [Consistent Complementary-Label Learning via Order-Preserving Losses](https://proceedings.mlr.press/v206/liu23g.html) (ICAIS 2023) 163 | - [Complementary Classifier Induced Partial Label Learning](https://arxiv.org/abs/2305.09897) (KDD 2023) 164 | - [Towards Effective Visual Representations for Partial-Label Learning](https://arxiv.org/abs/2305.06080) (CVPR 2023) 165 | - [Candidate-aware Selective Disambiguation Based On Normalized Entropy for Instance-dependent Partial-label Learning](https://ieeexplore.ieee.org/document/10376678) (ICCV 2023) 166 | - [Partial Label Learning with Dissimilarity Propagation guided Candidate Label Shrinkage](https://papers.nips.cc/paper_files/paper/2023/hash/6b97236d90d945be7c58268207a14f4f-Abstract-Conference.html) (NeurIPS 2023) 167 | - [Learning From Biased Soft Labels](https://arxiv.org/abs/2302.08155) (NeurIPS 2023) 168 | - [Meta Objective Guided Disambiguation for Partial Label Learning](https://arxiv.org/abs/2208.12459) (2023) 169 | - [Adversary-Aware Partial label learning with Label distillation](https://arxiv.org/abs/2304.00498) (2023) 170 | - [Solving Partial Label Learning Problem with Multi-Agent Reinforcement Learning ](https://openreview.net/forum?id=BNsuf5g-JRd) (2023) 171 | - [Learning from Stochastic Labels](https://arxiv.org/abs/2302.00299) (2023) 172 | - [Deep Duplex Learning for Weak Supervision](https://openreview.net/forum?id=SeZ5ONageGl) (2023) 173 | - [Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations](https://arxiv.org/abs/2305.12715) (2023) 174 | - [Self-distillation and self-supervision for partial label learning](https://www.sciencedirect.com/science/article/pii/S0031320323007136) (PR 2023) 175 | - [Partial Label Learning with a Partner](https://ojs.aaai.org/index.php/AAAI/article/view/29424) (AAAI 2024) 176 | - [Distilling Reliable Knowledge for Instance-Dependent Partial Label Learning](https://ojs.aaai.org/index.php/AAAI/article/view/29519) (AAAI 2024) 177 | - [Disentangled Partial Label Learning](https://ojs.aaai.org/index.php/AAAI/article/view/28976) (AAAI 2024) 178 | - [CroSel: Cross Selection of Confident Pseudo Labels for Partial-Label Learning](https://arxiv.org/abs/2303.10365) (CVPR 2024) 179 | - [A General Framework for Learning from Weak Supervision](https://arxiv.org/abs/2402.01922) (ICML 2024) 180 | - [Does Label Smoothing Help Deep Partial Label Learning?](https://openreview.net/forum?id=drjjxmi2Ha) (ICML 2024) 181 | - [Label Dropout: Improved Deep Learning Echocardiography Segmentation Using Multiple Datasets With Domain Shift and Partial Labelling](https://arxiv.org/abs/2403.07818) (SCIS 2024) 182 | - [Appeal: Allow Mislabeled Samples the Chance to be Rectified in Partial Label Learning ](https://arxiv.org/abs/2312.11034) (2024) 183 | - [Graph Partial Label Learning with Potential Cause Discovering](https://arxiv.org/abs/2403.11449) (2024) 184 | - [Reduction-based Pseudo-label Generation for Instance-dependent Partial Label Learning](https://arxiv.org/pdf/2410.20797) (2024) 185 | - [KMT-PLL: K-Means Cross-Attention Transformer for Partial Label Learning](https://ieeexplore.ieee.org/abstract/document/10384739) (TNNLS 2024) 186 | - [Diffusion Disambiguation Models for Partial Label Learning](https://arxiv.org/pdf/2507.00411) (2025) 187 | - [Tuning the Right Foundation Models is What you Need for Partial Label Learning](https://arxiv.org/pdf/2506.05027) (2025) 188 | - [Partial-Label Learning with Conformal Candidate Cleaning](https://arxiv.org/pdf/2502.07661) (UAI 2025) 189 | - [Exploiting the Potential Supervision Information of Clean Samples in Partial Label Learning](https://arxiv.org/pdf/2505.09354) (2025) 190 | - [Complementary Label Learning with Positive Label Guessing and Negative Label Enhancement](https://openreview.net/forum?id=LPRxGZ7Oax) (ICLR 2025) 191 | 192 | 193 | 194 | ### Partial-Multi Label Learning 195 | 196 | - [Learning a Deep ConvNet for Multi-label Classification with Partial Labels](https://arxiv.org/abs/1902.09720) (CVPR 2019) 197 | - [Multi-View Partial Multi-Label Learning with Graph-Based Disambiguation](https://ojs.aaai.org/index.php/AAAI/article/view/5761) (AAAI 2020) 198 | - [Partial Multi-Label Learning via Multi-Subspace Representation](https://www.ijcai.org/proceedings/2020/362) (IJCAI 2020) 199 | - [Feature-Induced Manifold Disambiguation for Multi-View Partial Multi-label Learning](https://dl.acm.org/doi/10.1145/3394486.3403098) (KDD 2020) 200 | - [Prior Knowledge Regularized Self-Representation Model for Partial Multilabel Learning](https://ieeexplore.ieee.org/document/9533180) (TYCB 2021) 201 | - [Global-Local Label Correlation for Partial Multi-Label Learning](https://ieeexplore.ieee.org/document/9343691) (TMM 2021) 202 | - [Progressive Enhancement of Label Distributions for Partial Multilabel Learning](https://ieeexplore.ieee.org/document/9615493) (TNNLS 2021) 203 | - [Partial Multi-Label Learning With Noisy Label Identification](https://ieeexplore.ieee.org/document/9354590) (TPAMI 2021) 204 | - [Partial Multi-Label Learning via Credible Label Elicitation](https://ieeexplore.ieee.org/document/9057438) (TPAMI 2021) 205 | - [Adversarial Partial Multi-Label Learning](https://arxiv.org/abs/1909.06717) (AAAI 2021) 206 | - [Learning from Complementary Labels via Partial-Output Consistency Regularization](https://www.ijcai.org/proceedings/2021/423) (IJCAI 2021) 207 | - [Partial Multi-Label Learning with Meta Disambiguation](https://dl.acm.org/doi/abs/10.1145/3447548.3467259) (KDD 2021) 208 | - [Understanding Partial Multi-Label Learning via Mutual Information](https://proceedings.neurips.cc/paper/2021/hash/217c0e01c1828e7279051f1b6675745d-Abstract.html) (NeurIPS 2021) 209 | - [Semantic-Aware Representation Blending for Multi-Label Image Recognition with Partial Labels](https://arxiv.org/abs/2203.02172) (AAAI 2022) 210 | - [Structured Semantic Transfer for Multi-Label Recognition with Partial Labels)](https://arxiv.org/abs/2112.10941) (AAAI 2022) 211 | - [Boosting Multi-Label Image Classification with Complementary Parallel Self-Distillation](https://arxiv.org/abs/2205.10986) (IJCAI 2022) 212 | - [Multi-label Classification with Partial Annotations using Class-aware Selective Loss](https://arxiv.org/abs/2110.10955) (CVPR 2022) 213 | - [Deep Double Incomplete Multi-View Multi-Label Learning With Incomplete Labels and Missing Views](https://ieeexplore.ieee.org/document/10086538) (TNNLS 2023) 214 | - [Towards Enabling Binary Decomposition for Partial Multi-Label Learning](https://ieeexplore.ieee.org/document/10168295) (TPAMI 2023) 215 | - [Deep Partial Multi-Label Learning with Graph Disambiguation](https://arxiv.org/abs/2305.05882) (IJCAI 2023) 216 | - [Learning in Imperfect Environment: Multi-Label Classification with Long-Tailed Distribution and Partial Labels](https://arxiv.org/abs/2304.10539) (ICCV 2023) 217 | - [Partial Multi-Label Learning with Probabilistic Graphical Disambiguation](https://papers.nips.cc/paper_files/paper/2023/hash/04e05ba5cbc36044f6499d1edf15247e-Abstract-Conference.html) (NeurIPS 2023) 218 | - [ProPML: Probability Partial Multi-label Learning](https://arxiv.org/abs/2403.07603) (DSAA 2023) 219 | - [A Deep Model for Partial Multi-Label Image Classification with Curriculum Based Disambiguation](https://arxiv.org/abs/2207.02410) (ML 2024) 220 | - [Partial Multi-View Multi-Label Classification via Semantic Invariance Learning and PrototypeModeling](https://icml.cc/virtual/2024/poster/34972) (ICML 2024) 221 | - [Reliable Representations Learning for Incomplete Multi-View Partial Multi-Label Classification](https://arxiv.org/abs/2303.17117) (2024) 222 | - [PLMCL: Partial-Label Momentum Curriculum Learning for Multi-Label Image Classification](https://arxiv.org/abs/2208.09999) (2024) 223 | - [Combining Supervised Learning and Reinforcement Learning for Multi-Label Classification Tasks with Partial Labels](https://arxiv.org/abs/2406.16293) (2024) 224 | - [Free Performance Gain from Mixing Multiple Partially Labeled Samples in Multi-label Image Classification](https://arxiv.org/abs/2405.15860) (2024) 225 | - [Noise-Resistant Label Reconstruction Feature Selection for Partial Multi-Label Learning](https://arxiv.org/pdf/2506.04669) (IJCAI 2025) 226 | - [WPML3CP: Wasserstein Partial Multi-Label Learning with Dual Label Correlation Perspectives]() (IJCAI2025) 227 | - [Partial Multi-View Multi-Label Classification via Semantic Invariance Learning and Prototype Modeling](https://proceedings.mlr.press/v235/liu24bv.html) (ICML2024) 228 | - [Revisiting Sparsity Constraint Under High-Rank Property in Partial Multi-Label Learning](https://arxiv.org/pdf/2505.20938) (2025) 229 | - 230 | 231 | 232 | 233 | ### Noisy Partial Label Learning 234 | 235 | - [PiCO+: Contrastive Label Disambiguation for Robust Partial Label Learning](https://arxiv.org/abs/2201.08984) (TPAMI 2023) 236 | - [On the Robustness of Average Losses for Partial-Label Learning](https://arxiv.org/abs/2106.06152) (TPAMI 2023) 237 | - [FREDIS: A Fusion Framework of Refinement and Disambiguation for Unreliable Partial Label Learning](https://proceedings.mlr.press/v202/qiao23b.html) (ICML 2023) 238 | - [Unreliable Partial Label Learning with Recursive Separation](https://arxiv.org/abs/2302.09891) (IJCAI 2023) 239 | - [ALIM: Adjusting Label Importance Mechanism for Noisy Partial Label Learning](https://arxiv.org/abs/2301.12077) (NeurIPS 2023) 240 | - [IRNet: Iterative Refinement Network for Noisy Partial Label Learning](https://arxiv.org/abs/2211.04774) (2023) 241 | - [Robust Representation Learning for Unreliable Partial Label Learning](https://arxiv.org/abs/2308.16718) (2023) 242 | - [Pseudo-labelling meets Label Smoothing for Noisy Partial Label Learning](https://arxiv.org/abs/2402.04835) (2024) 243 | - [Pre-trained Vision-Language Models Assisted Noisy Partial Label Learning](https://arxiv.org/pdf/2506.03229) (2025) 244 | - [Noise Separation guided Candidate Label Reconstruction for Noisy Partial Label Learning]() (ICLR 2025) 245 | 246 | 247 | 248 | ### Semi-Supervised Partial Label Learning 249 | 250 | - [Semi-Supervised Partial Label Learning via Confidence-Rated Margin Maximization](https://proceedings.neurips.cc/paper/2020/hash/4dea382d82666332fb564f2e711cbc71-Abstract.html) (NeurIPS 2020) 251 | - [Exploiting Unlabeled Data via Partial Label Assignment for Multi-Class Semi-Supervised Learning](https://ojs.aaai.org/index.php/AAAI/article/view/17310) (AAAI 2021) 252 | - [Distributed Semisupervised Partial Label Learning Over Networks](https://ieeexplore.ieee.org/document/9699063) (AI 2022) 253 | - [Learning with Partial-Label and Unlabeled Data: A Uniform Treatment for Supervision Redundancy and Insufficiency](https://proceedings.mlr.press/v235/liu24ar.html) (ICML 2024) 254 | - [FairMatch: Promoting Partial Label Learning by Unlabeled Samples](https://dl.acm.org/doi/abs/10.1145/3637528.3671685) (KDD 2024) 255 | 256 | 257 | 258 | ### Multi-Instance Partial Label Learning 259 | 260 | - [Multi-Instance Partial-Label Learning: Towards Exploiting Dual Inexact Supervision](https://arxiv.org/abs/2212.08997) (SCIS 2023) 261 | - [Disambiguated Attention Embedding for Multi-Instance Partial-Label Learning](https://arxiv.org/abs/2305.16912) (NeurIPS 2023) 262 | - [Exploiting Conjugate Label Information for Multi-Instance Partial-Label Learning](https://arxiv.org/abs/2408.14369) (IJCAI 2024) 263 | - [On Characterizing and Mitigating Imbalances in Multi-Instance Partial Label Learning](https://arxiv.org/abs/2407.10000) (2024) 264 | - [Multi-Instance Partial-Label Learning with Margin Adjustment](https://arxiv.org/pdf/2501.12597) (NeurIPS 2024) 265 | 266 | 267 | 268 | ### Imblanced Partial Label Learning 269 | 270 | - [Towards Mitigating the Class-Imbalance Problem for Partial Label Learning](https://dl.acm.org/doi/10.1145/3219819.3220008) (KDD 2018) [:octocat:](https://github.com/seu71wj/CIMAP) 271 | - [A Partial Label Metric Learning Algorithm for Class Imbalanced Data](https://proceedings.mlr.press/v157/liu21f.html) (ACML 2021) 272 | - [SoLar: Sinkhorn Label Refinery for Imbalanced Partial-Label Learning](https://arxiv.org/abs/2209.10365) (NeurIPS 2022) 273 | - [Class-Imbalanced Complementary-Label Learning via Weighted Loss](https://arxiv.org/abs/2209.14189) (NN 2023) 274 | - [Long-Tailed Partial Label Learning via Dynamic Rebalancing](https://arxiv.org/abs/2302.05080) (ICLR 2023) 275 | - [Long-Tailed Partial Label Learning by Head Classifier and Tail Classifier Cooperation](https://ojs.aaai.org/index.php/AAAI/article/view/29182) (AAAI 2024) 276 | - [GBRIP: Granular Ball Representation for Imbalanced Partial Label Learning]() 277 | - [Pseudo Labels Regularization for Imbalanced Partial-Label Learning ](https://arxiv.org/abs/2303.03946) (ICASSP 2024) 278 | - 279 | 280 | 281 | 282 | ### Out-of-distribution Partial Label Learning 283 | 284 | - [Out-of-distribution Partial Label Learning](https://arxiv.org/abs/2403.06681) (2024) 285 | 286 | 287 | 288 | ### Federated Partial Label Learning 289 | 290 | - [Federated Partial Label Learning with Local-Adaptive Augmentation and Regularization](https://ojs.aaai.org/index.php/AAAI/article/view/29562) (AAAI 2024) 291 | 292 | 293 | 294 | ### Partial Label Regression 295 | 296 | - [Partial-Label Regression](https://arxiv.org/abs/2306.08968) (AAAI 2023) 297 | - [Partial-Label Learning with a Reject Option](https://arxiv.org/abs/2402.00592) (TMLR 2024) 298 | 299 | 300 | 301 | ### Dimensionality Reduction 302 | 303 | - [Partial Label Dimensionality Reduction via Confidence-Based Dependence Maximization](https://dl.acm.org/doi/abs/10.1145/3447548.3467313) (KDD 2021) 304 | - [Disambiguation Enabled Linear Discriminant Analysis for Partial Label Dimensionality Reduction](https://dl.acm.org/doi/abs/10.1145/3494565) (TKDD 2022) 305 | - [Submodular Feature Selection for Partial Label Learning](https://dl.acm.org/doi/abs/10.1145/3534678.3539292) (KDD 2022) 306 | - [Dimensionality Reduction for Partial Label Learning: A Unified and Adaptive Approach](https://www.computer.org/csdl/journal/tk/2024/08/10440495/1UGSdp8q3Wo) (TKDE 2024) 307 | 308 | 309 | 310 | ### Multi-Complementary Label Learning 311 | 312 | - [Learning with Multiple Complementary Labels](https://arxiv.org/abs/1912.12927) (ICML 2020) 313 | - [Multi-Complementary and Unlabeled Learning for Arbitrary Losses and Models](https://arxiv.org/abs/2001.04243) (PR 2022) 314 | 315 | 316 | 317 | ### Multi-View Learning 318 | 319 | - [Deep Partial Multi-View Learning](https://ieeexplore.ieee.org/document/9258396) (TPAMI 2022) 320 | 321 | 322 | 323 | ### Adversarial Training 324 | 325 | - [Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks](https://openreview.net/forum?id=s7SukMH7ie9) (NeurIPS 2022) 326 | 327 | 328 | 329 | ### Negative Learning 330 | 331 | - [NLNL: Negative Learning for Noisy Labels](https://arxiv.org/abs/1908.07387) (ICCV 2019) 332 | 333 | 334 | 335 | ### Incremental Learning 336 | 337 | - [Partial label learning with emerging new labels](https://link.springer.com/article/10.1007/s10994-022-06244-2) (ML 2022) 338 | - [Complementary Labels Learning with Augmented Classes](https://arxiv.org/abs/2211.10701) （2022） 339 | - [An Unbiased Risk Estimator for Partial Label Learning with Augmented Classes](https://arxiv.org/pdf/2409.19600) (2024) 340 | 341 | 342 | 343 | ### Online Learning 344 | 345 | - [Online Partial Label Learning](https://dl.acm.org/doi/abs/10.1007/978-3-030-67661-2_27) (ECML-PKDD 2020) 346 | 347 | 348 | 349 | ### Conformal Prediction 350 | 351 | - [Conformal Prediction with Partially Labeled Data](https://arxiv.org/abs/2306.01191) (SCPPA 2023) 352 | 353 | 354 | 355 | ### Few-Shot Learning 356 | 357 | - [Few-Shot Partial-Label Learning](https://arxiv.org/abs/2106.00984) (IJCAI 2021) 358 | 359 | 360 | 361 | ### Open-set Problem 362 | 363 | - [Partial-label Learning with Mixed Closed-set and Open-set Out-of-candidate Examples](https://arxiv.org/abs/2307.00553) (KDD 2023) 364 | 365 | 366 | 367 | ### Data Augmentation 368 | 369 | - [Partial Label Learning with Discrimination Augmentation](https://dl.acm.org/doi/abs/10.1145/3534678.3539363) (KDD 2022) [:octocat:](https://github.com/wwangwitsel/PLDA) 370 | - [Enhancing Label Sharing Efficiency in Complementary-Label Learning with Label Augmentation](https://arxiv.org/abs/2305.08344) (2023) 371 | 372 | 373 | 374 | ### Multi-Dimensional 375 | 376 | - [Learning From Multi-Dimensional Partial Labels](https://www.ijcai.org/proceedings/2020/407) (IJCAI 2020) 377 | 378 | 379 | 380 | ### Domain Adaptation 381 | 382 | - [Partial Label Unsupervised Domain Adaptation with Class-Prototype Alignment](https://openreview.net/forum?id=jpq0qHggw3t) (ICLR 2023) 383 | 384 | 385 | 386 | ## Applications 387 | 388 | 389 | 390 | ### Audio 391 | 392 | - [Semi-Supervised Audio Classification with Partially Labeled Data ](https://arxiv.org/abs/2111.12761)(2021) 393 | 394 | 395 | 396 | ### Text 397 | 398 | - [Complementary Auxiliary Classifiers for Label-Conditional Text Generation](https://ojs.aaai.org/index.php/AAAI/article/view/6346) (AAAI 2020) 399 | 400 | 401 | 402 | ### Sequence 403 | 404 | - [Star Temporal Classification: Sequence Classification with Partially Labeled Data](https://arxiv.org/abs/2201.12208) (2022) 405 | 406 | 407 | 408 | ### Recognition 409 | 410 | - [`Webly-Supervised` Fine-Grained Recognition with Partial Label Learning](https://www.ijcai.org/proceedings/2022/209) (IJCAI 2022) [:octocat:](https://github.com/msfuxian/WSFGPLL) 411 | - [Partial Label Learning with Focal Loss for Sea Ice Classification Based on Ice Charts](https://arxiv.org/abs/2406.03645) (AEORS 2023) 412 | - [Partial Label Learning for Emotion Recognition from EEG](https://arxiv.org/abs/2302.13170) (2023) 413 | - [A Confidence-based Partial Label Learning Model for Crowd-Annotated Named Entity Recognition](https://arxiv.org/abs/2305.12485) (ACL 2023) [:octocat:](https://github.com/LemXiong/CPLL?tab=readme-ov-file) 414 | 415 | 416 | 417 | ### Object Localization 418 | 419 | - [Adversarial Complementary Learning for Weakly Supervised Object Localization](https://arxiv.org/abs/1804.06962) (CVPR 2018) [:octocat:](https://github.com/halbielee/ACoL_pytorch) 420 | - [Learning to Detect Instance-level Salient Objects Using Complementary Image Labels](https://arxiv.org/abs/2111.10137) (2021) 421 | 422 | 423 | 424 | ### Map Reconstruction 425 | 426 | - [Deep Learning with Partially Labeled Data for Radio Map Reconstruction](https://arxiv.org/abs/2306.05294) (2023) 427 | 428 | 429 | 430 | ### Semi-supervised Learning 431 | 432 | - [Boosting Semi-Supervised Learning with Contrastive Complementary Labeling](https://arxiv.org/abs/2212.06643) (NN 2023) 433 | - [Controller-Guided Partial Label Consistency Regularization with Unlabeled Data](https://arxiv.org/abs/2210.11194) (2024) 434 | - [Semi-supervised Contrastive Learning Using Partial Label Information](https://arxiv.org/abs/2003.07921) (2024) 435 | 436 | 437 | 438 | ### Active Learning 439 | 440 | - [Exploiting counter-examples for active learning with partial labels](https://link.springer.com/article/10.1007/s10994-023-06485-9) (ML 2023) [:octocat:](https://github.com/Ferenas/APLL) 441 | - [Active Learning with Partial Labels](https://openreview.net/forum?id=1VuBdlNBuR) (2023) 442 | 443 | 444 | 445 | ### Noisy Label Learning 446 | 447 | - [Learning from Noisy Labels with Complementary Loss Functions](https://ojs.aaai.org/index.php/AAAI/article/view/17213) (AAAI 2021) 448 | - [Adaptive Integration of Partial Label Learning and Negative Learning for Enhanced Noisy Label Learning](https://arxiv.org/abs/2312.09505) (AAAI 2024) 449 | - [Partial Label Supervision for Agnostic Generative Noisy Label Learning](https://arxiv.org/abs/2308.01184) (2024) 450 | 451 | 452 | 453 | ### Test-Time Adaptation 454 | 455 | - [Rethinking Precision of Pseudo Label: Test-Time Adaptation via Complementary Learning](https://arxiv.org/abs/2301.06013) (2023) 456 | 457 | 458 | 459 | 460 | 461 | 462 | ## Dataset 463 | 464 | ### Tabular Dataset: 465 | 466 | **Notice:** The following partial label learning data sets were collected and pre-processed by Prof. [Min-Ling Zhang](https://palm.seu.edu.cn/zhangml/), with courtesy and proprietary to the authors of referred literatures on them. The pre-processed data sets can be used at your own risk and for academic purpose only. More information can be found in [here](http://palm.seu.edu.cn/zhangml/). 467 | 468 | Dataset for partial label learning: 469 | 470 | | [FG-NET](https://palm.seu.edu.cn/zhangml/files/FG-NET.rar) | [Lost](https://palm.seu.edu.cn/zhangml/files/lost.rar) | [MSRCv2](https://palm.seu.edu.cn/zhangml/files/MSRCv2.rar) | [BirdSong](https://palm.seu.edu.cn/zhangml/files/BirdSong.rar) | [Soccer Player](https://palm.seu.edu.cn/zhangml/files/SoccerPlayer.rar) | [Yahoo! News](https://palm.seu.edu.cn/zhangml/files/Yahoo!News.rar) | [Mirflickr](https://palm.seu.edu.cn/zhangml/files/Mirflickr.zip) | 471 | | ---------------------------------------------------------- | ------------------------------------------------------ | ---------------------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | 472 | 473 | Dataset for partial multi-label learning: 474 | 475 | | [Music_emotion](https://palm.seu.edu.cn/zhangml/files/pml_music_emotion.zip) | [Music_style](https://palm.seu.edu.cn/zhangml/files/pml_music_style.zip) | [Mirflickr](https://palm.seu.edu.cn/zhangml/files/pml_mirflickr.zip) | [YeastBP](https://palm.seu.edu.cn/zhangml/files/pml_YeastBP.zip) | [YeastCC](https://palm.seu.edu.cn/zhangml/files/pml_YeastCC.zip) | [YeastMF](https://palm.seu.edu.cn/zhangml/files/pml_YeastMF.zip) | 476 | | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | 477 | 478 | Data sets for multi-instance partial-label learning: 479 | 480 | | [MNIST](https://palm.seu.edu.cn/zhangml/files/MNIST_MIPL.zip) | [FMNIST](https://palm.seu.edu.cn/zhangml/files/FMNIST_MIPL.zip) | [Newsgroups](https://palm.seu.edu.cn/zhangml/files/Newsgroups_MIPL.zip) | [Birdsong](https://palm.seu.edu.cn/zhangml/files/Birdsong_MIPL.zip) | [SIVAL](https://palm.seu.edu.cn/zhangml/files/SIVAL_MIPL.zip) | [CRC-Row](https://palm.seu.edu.cn/zhangml/files/CRC-MIPL-Row.zip) | [CRC-SBN](https://palm.seu.edu.cn/zhangml/files/CRC-MIPL-SBN.zip) | [CRC-KMeansSeg](https://palm.seu.edu.cn/zhangml/files/CRC-MIPL-KMeansSeg.zip) | [CRC-SIFT](https://palm.seu.edu.cn/zhangml/files/CRC-MIPL-SIFT.zip) | 481 | | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | 482 | 483 | 484 | 485 | ### Image Dataset: 486 | 487 | - Dataset for complementary-label learning: [CLImage dataset](https://github.com/ntucllab/CLImage_Dataset) 488 | - Dataset for partial-label learning: [PLENCH dataset](https://github.com/wwangwitsel/PLENCH) 489 | 490 | 491 | 492 | 493 | 494 | 495 | 496 | ## Star History 497 | 498 | [![Star History Chart](https://api.star-history.com/svg?repos=wu-dd/Advances-in-Partial-and-Complementary-Label-Learning&type=Date)](https://star-history.com/#wu-dd/Advances-in-Partial-and-Complementary-Label-Learning&Date) 499 | 500 | ## Citing Advances in Partial/Complementary Label Learning 501 | 502 | If you find this project useful for your research, please use the following BibTeX entry. 503 | 504 | ``` 505 | @misc{Wu2022advances, 506 | author={Dong-Dong Wu}, 507 | title={Advances in Partial/Complementary Label Learning }, 508 | howpublished={\url{wu-dd/Advances-in-Partial-and-Complementary-Label-Learning}}, 509 | year={2022} 510 | } 511 | ``` 512 | 513 | ## Acknowledgments 514 | --------------------------------------------------------------------------------