├── _img ├── ss.png ├── GAN.png ├── intro.png ├── teaser.gif ├── SUBSCRIBE.mp4 ├── subscribe.gif ├── subscribe.png ├── supervised.gif ├── baselinegan.png ├── deeplearning.png ├── mainpage │ ├── RNN.png │ ├── CNNs.png │ ├── RNNs1.png │ ├── books.jpg │ ├── logo.gif │ ├── logo.png │ ├── models.png │ ├── online.png │ ├── star_1.png │ ├── star_2.png │ ├── star_3.png │ ├── star_4.png │ ├── star_5.png │ ├── article.jpeg │ ├── tutorial.png │ ├── utility.gif │ ├── utility.png │ ├── utility2.gif │ ├── autoencoder.png │ ├── generative.png │ ├── goingdeep.gif │ ├── logo (copy).gif │ ├── logo (copy).png │ ├── readingdata.gif │ ├── variables.gif │ ├── Blogger_icon.png │ ├── convolutional.png │ ├── installation.gif │ ├── multiple_gpu.png │ ├── TensorFlow_World.gif │ ├── follow-twitter.gif │ ├── generative_model.png │ ├── gettingstarted.gif │ ├── logisticregression.png │ ├── progress-overall-0.png │ ├── Autoencoder_structure.png │ ├── progress-overall-100.png │ ├── progress-overall-20.png │ ├── 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https://raw.githubusercontent.com/instillai/Generative-Adversarial-Networks-Roadmap/HEAD/_img/mainpage/representationlearning.png -------------------------------------------------------------------------------- /_img/mainpage/Reinforcement_learning_diagram.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/instillai/Generative-Adversarial-Networks-Roadmap/HEAD/_img/mainpage/Reinforcement_learning_diagram.png -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2019 Machine Learning Mindset 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 | -------------------------------------------------------------------------------- /conf.py: -------------------------------------------------------------------------------- 1 | import os 2 | import sys 3 | 4 | import sphinx_rtd_theme 5 | sys.path.insert(0, os.path.abspath('_ext')) 6 | # from recommonmark.parser import CommonMarkParser 7 | 8 | sys.path.insert(0, os.path.abspath('..')) 9 | sys.path.append(os.path.dirname(__file__)) 10 | # os.environ.setdefault("DJANGO_SETTINGS_MODULE", "readthedocs.settings.dev") 11 | 12 | # from django.conf import settings 13 | 14 | # import django 15 | # django.setup() 16 | 17 | 18 | sys.path.append(os.path.abspath('_ext')) 19 | extensions = [ 20 | 'sphinx.ext.autodoc', 21 | 'sphinx.ext.intersphinx', 22 | ] 23 | templates_path = ['_templates'] 24 | 25 | edit_on_github_project = 'astorfi/Deep-Learning-NLP' 26 | 27 | edit_on_github_branch = 'master' 28 | 29 | source_suffix = ['.rst', '.md'] 30 | # source_parsers = { 31 | # '.md': CommonMarkParser, 32 | # } 33 | 34 | master_doc = 'index' 35 | project = u'Deep-Learning-NLP' 36 | copyright = u'2018, Amirsina Torfi' 37 | author = u'Amirsina Torfi' 38 | version = '1.0' 39 | release = '1.0' 40 | exclude_patterns = ['_build'] 41 | default_role = 'obj' 42 | pygments_style = 'sphinx' 43 | # intersphinx_mapping = { 44 | # 'TensorFlow-World': ('https://github.com/astorfi/TensorFlow-World', None), 45 | # } 46 | htmlhelp_basename = 'ReadTheDocsdoc' 47 | latex_documents = [ 48 | (master_doc, 'Deep-Learning-NLP.tex', u'Deep-Learning-NLP Documentation', 49 | u'Amirsina Torfi', 'manual'), 50 | ] 51 | man_pages = [ 52 | (master_doc, 'Deep-Learning-NLP', u'Deep-Learning-NLP Documentation', 53 | [author], 1) 54 | ] 55 | exclude_patterns = [ 56 | # 'api' # needed for ``make gettext`` to not die. 57 | ] 58 | 59 | language = 'en' 60 | 61 | locale_dirs = [ 62 | 'locale/', 63 | ] 64 | gettext_compact = False 65 | 66 | html_theme = 'sphinx_rtd_theme' 67 | html_static_path = ['_static'] 68 | html_theme_path = [sphinx_rtd_theme.get_html_theme_path()] 69 | html_logo = 'logo/logo.png' 70 | html_theme_options = { 71 | 'logo_only': True, 72 | 'display_version': False, 73 | } 74 | 75 | github_url='https://github.com/astorfi/Deep-Learning-NLP' 76 | 77 | html_context = { 78 | "display_github": True, # Add 'Edit on Github' link instead of 'View page source' 79 | "last_updated": True, 80 | "commit": False, 81 | 'github_url': 'https://github.com/astorfi/Deep-Learning-NLP' 82 | } 83 | 84 | 85 | #def setup(app): 86 | # app.add_stylesheet('custom.css') 87 | -------------------------------------------------------------------------------- /CODE_OF_CONDUCT.rst: -------------------------------------------------------------------------------- 1 | Contributor Covenant Code of Conduct 2 | ==================================== 3 | 4 | Our Pledge 5 | ---------- 6 | 7 | In the interest of fostering an open and welcoming environment, we as contributors and maintainers pledge to making participation in our project and our community a harassment-free experience for everyone, regardless of age, body size, disability, ethnicity, gender identity and expression, level of experience, nationality, personal appearance, race, religion, or sexual identity and orientation. 8 | 9 | Our Standards 10 | ------------- 11 | 12 | Examples of behavior that contributes to creating a positive environment include: 13 | 14 | * Using welcoming and inclusive language 15 | * Being respectful of differing viewpoints and experiences 16 | * Gracefully accepting constructive criticism 17 | * Focusing on what is best for the community 18 | * Showing empathy towards other community members 19 | 20 | Examples of unacceptable behavior by participants include: 21 | 22 | * The use of sexualized language or imagery and unwelcome sexual attention or advances 23 | * Trolling, insulting/derogatory comments, and personal or political attacks 24 | * Public or private harassment 25 | * Publishing others' private information, such as a physical or electronic address, without explicit permission 26 | * Other conduct which could reasonably be considered inappropriate in a professional setting 27 | 28 | Our Responsibilities 29 | -------------------- 30 | 31 | Project maintainers are responsible for clarifying the standards of acceptable behavior and are expected to take appropriate and fair corrective action in response to any instances of unacceptable behavior. 32 | 33 | Project maintainers have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, or to ban temporarily or permanently any contributor for other behaviors that they deem inappropriate, threatening, offensive, or harmful. 34 | 35 | Scope 36 | ----- 37 | 38 | This Code of Conduct applies both within project spaces and in public spaces when an individual is representing the project or its community. Examples of representing a project or community include using an official project e-mail address, posting via an official social media account, or acting as an appointed representative at an online or offline event. Representation of a project may be further defined and clarified by project maintainers. 39 | 40 | Enforcement 41 | ----------- 42 | 43 | Instances of abusive, harassing, or otherwise unacceptable behavior may be reported by contacting the project team at amirsina.torfi@gmail.com. The project team will review and investigate all complaints, and will respond in a way that it deems appropriate to the circumstances. The project team is obligated to maintain confidentiality with regard to the reporter of an incident. Further details of specific enforcement policies may be posted separately. 44 | 45 | Project maintainers who do not follow or enforce the Code of Conduct in good faith may face temporary or permanent repercussions as determined by other members of the project's leadership. 46 | 47 | Attribution 48 | ------------ 49 | 50 | This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4, available at [http://contributor-covenant.org/version/1/4][version] 51 | 52 | [homepage]: http://contributor-covenant.org 53 | [version]: http://contributor-covenant.org/version/1/4/ 54 | -------------------------------------------------------------------------------- /_img/mainpage/Recurrent_neural_network_unfold.svg: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 19 | 21 | 29 | 35 | 36 | 44 | 50 | 51 | 60 | 66 | 67 | 76 | 82 | 83 | 92 | 98 | 99 | 108 | 114 | 115 | 124 | 130 | 131 | 139 | 145 | 146 | 154 | 160 | 161 | 169 | 175 | 176 | 184 | 190 | 191 | 200 | 206 | 207 | 215 | 221 | 222 | 223 | 243 | 245 | 246 | 248 | image/svg+xml 249 | 251 | 252 | 253 | 254 | 255 | 260 | 263 | 272 | 278 | 284 | 290 | 295 | 300 | x 312 | h 324 | o 336 | U 348 | V 360 | W 372 | 373 | 379 | Unfold 391 | 394 | 397 | 400 | 409 | 415 | 421 | 426 | 431 | xt-1 445 | ht-1 459 | ot-1 473 | U 485 | W 497 | 502 | 507 | 512 | 517 | 518 | 521 | 530 | 536 | 542 | 547 | 552 | xt 566 | ht 580 | ot 594 | U 606 | W 618 | 619 | 628 | 634 | 640 | 645 | 650 | xt+1 664 | ht+1 678 | ot+1 692 | U 704 | W 716 | 717 | V 729 | V 741 | V 753 | V 765 | ... 778 | ... 791 | 792 | 793 | 794 | -------------------------------------------------------------------------------- /README.rst: -------------------------------------------------------------------------------- 1 | ################################################### 2 | GANs World Resources 3 | ################################################### 4 | 5 | .. image:: https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat 6 | :target: https://github.com/machinelearningmindset/GANs-Roadmap/pulls 7 | .. image:: https://badges.frapsoft.com/os/v2/open-source.png?v=103 8 | :target: https://github.com/ellerbrock/open-source-badge/ 9 | .. image:: https://img.shields.io/badge/Made%20with-Python-1f425f.svg 10 | :target: https://www.python.org/ 11 | .. image:: https://img.shields.io/pypi/l/ansicolortags.svg 12 | :target: https://github.com/astorfi/Awsome-GANs-World/blob/master/LICENSE 13 | .. image:: https://img.shields.io/twitter/follow/machinemindset.svg?label=Follow&style=social 14 | :target: https://twitter.com/machinemindset 15 | 16 | 17 | To support maintaining and upgrading this project, please kindly consider `Sponsoring the project developer `_. 18 | 19 | Any level of support is a great contribution here :heart: 20 | 21 | .. raw:: html 22 | 23 |
24 | 25 | .. raw:: html 26 | 27 | 28 | 29 | 30 | 31 | .. raw:: html 32 | 33 |
34 | 35 | 36 | 37 | ################################################### 38 | Slack Group 39 | ################################################### 40 | 41 | .. raw:: html 42 | 43 |
44 | 45 | .. raw:: html 46 | 47 | 48 | 49 | 50 | 51 | .. raw:: html 52 | 53 |
54 | 55 | 56 | ################## 57 | Table of Contents 58 | ################## 59 | .. contents:: 60 | :local: 61 | :depth: 4 62 | 63 | *************** 64 | Introduction 65 | *************** 66 | 67 | The purpose of this project is to introduce a shortcut to developers and researcher 68 | for finding useful resources about ``Generative Adversarial Networks``. 69 | 70 | .. image:: _img/baselinegan.png 71 | 72 | ============ 73 | Motivation 74 | ============ 75 | 76 | There are different motivations for this open source project. 77 | 78 | .. -------------------- 79 | .. Why Deep Learning? 80 | .. -------------------- 81 | 82 | ------------------------------------------------------------ 83 | What's the point of this open source project? 84 | ------------------------------------------------------------ 85 | 86 | The organization of the resources is such that the user can easily find the things he/she is looking for. 87 | We divided the resources to a large number of categories that in the beginning one may 88 | have a headache!!! However, if someone knows what is being located, it is very easy to find the most related resources. 89 | Even if someone doesn't know what to look for, in the beginning, the list of resources have 90 | been provided. 91 | 92 | 93 | .. ================================================ 94 | .. How to make the most of this effort 95 | .. ================================================ 96 | 97 | ************ 98 | Papers 99 | ************ 100 | 101 | .. image:: _img/mainpage/article.jpeg 102 | 103 | This chapter is associated with the papers published associated with generative adversarial Networks. 104 | 105 | ======================================== 106 | Types and Models 107 | ======================================== 108 | 109 | .. image:: _img/GAN.png 110 | Image by: `Rouzbeh Asghari Shirvani `_ 111 | 112 | 113 | 114 | ---------------------------------------------------- 115 | Core: Generative Adversarial Networks (VanillaGAN) 116 | ---------------------------------------------------- 117 | 118 | 119 | .. _ganpaper: http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf 120 | .. _gancode: https://github.com/goodfeli/adversarial 121 | 122 | .. _energyganpaper: https://arxiv.org/pdf/1609.03126v2.pdf 123 | .. _energygancode: https://github.com/buriburisuri/ebgan 124 | 125 | .. _convergepaper: https://arxiv.org/pdf/1801.04406.pdf 126 | .. _convergecode: https://github.com/LMescheder/GAN_stability 127 | 128 | 129 | 130 | +--------------------------------------------------------------------+-------------------------------+---------------------------+------------------------------------------------+ 131 | | Title | Text | Software | Citation | 132 | +====================================================================+===============================+===========================+================================================+ 133 | | Generative Adversarial Nets | `Paper `_ | `Code `_ | Rate :star2: :star2: :star2: :star2: :star2: | 134 | +--------------------------------------------------------------------+-------------------------------+---------------------------+------------------------------------------------+ 135 | | ENERGY-BASED GENERATIVE ADVERSARIAL NETWORK | `Paper `_ | `Code `_ | Rate :star2: :star2: :star2: :star2: | 136 | +--------------------------------------------------------------------+-------------------------------+---------------------------+------------------------------------------------+ 137 | | Which Training Methods for GANs do Actually Converge | `Paper `_ | `Code `_ | Rate :star2: :star2: | 138 | +--------------------------------------------------------------------+-------------------------------+---------------------------+------------------------------------------------+ 139 | 140 | 141 | 142 | ---------------------------------------------------------- 143 | Conditional Generative Adversarial Networks (CGAN) 144 | ---------------------------------------------------------- 145 | 146 | .. _conganpaper: https://arxiv.org/abs/1411.1784 147 | .. _congancode: https://github.com/zhangqianhui/Conditional-GAN 148 | 149 | .. _photorealpaper: https://arxiv.org/pdf/1609.04802.pdf 150 | .. _photorealcode: https://github.com/tensorlayer/srgan 151 | 152 | .. _im2impaper: https://arxiv.org/abs/1611.07004 153 | .. _im2imcode: https://github.com/phillipi/pix2pix 154 | 155 | .. _vismanpaper: https://arxiv.org/abs/1609.03552 156 | .. _vismancode: https://github.com/junyanz/iGAN 157 | 158 | 159 | 160 | 161 | +--------------------------------------------------------------------+-------------------------------+---------------------------+------------------------------------------------+ 162 | | Title | Text | Software | Citation | 163 | +====================================================================+===============================+===========================+================================================+ 164 | | Conditional generative adversarial nets | `Paper `_ | `Code `_ | Rate :star2: :star2: :star2: :star2: :star2: | 165 | +--------------------------------------------------------------------+-------------------------------+---------------------------+------------------------------------------------+ 166 | | Photo-realistic single image super-resolution using a GAN | `Paper `_ | `Code `_ | Rate :star2: :star2: :star2: :star2: | 167 | +--------------------------------------------------------------------+-------------------------------+---------------------------+------------------------------------------------+ 168 | | Image-to-Image Translation with Conditional Adversarial Networks | `Paper `_ | `Code `_ | Rate :star2: :star2: :star2: :star2: | 169 | +--------------------------------------------------------------------+-------------------------------+---------------------------+------------------------------------------------+ 170 | | Generative Visual Manipulation on the Natural Image Manifold | `Paper `_ | `Code `_ | Rate :star2: :star2: | 171 | +--------------------------------------------------------------------+-------------------------------+---------------------------+------------------------------------------------+ 172 | 173 | 174 | 175 | 176 | ------------------------------------------------------ 177 | Laplacian Pyramid of Adversarial Networks (LAPGAN) 178 | ------------------------------------------------------ 179 | 180 | .. _laplacialganpaper: http://papers.nips.cc/paper/5773-deep-generative-image-models-using-a-laplacian-pyramid-of-adversarial-networks.pdf 181 | .. _laplacialgancode: https://github.com/witnessai/LAPGAN 182 | 183 | +--------------------------------------------------------------------------------+---------------------------------+---------------------------------+------------------------------------------------+ 184 | | Title | Text | Software | Citation | 185 | +================================================================================+=================================+=================================+================================================+ 186 | | Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks | `Paper `_ | `Code `_ | Rate :star2: :star2: :star2: :star2: :star2: | 187 | +--------------------------------------------------------------------------------+---------------------------------+---------------------------------+------------------------------------------------+ 188 | 189 | 190 | ------------------------------------------------------------- 191 | Deep Convolutional Generative Adversarial Networks (DCGAN) 192 | ------------------------------------------------------------- 193 | 194 | .. _convganpaper: https://arxiv.org/abs/1511.06434 195 | .. _convgancode: https://github.com/Newmu/dcgan_code 196 | 197 | .. _text2imgpaper: https://arxiv.org/pdf/1605.05396.pdf 198 | .. _text2imgcode: https://github.com/reedscot/icml2016 199 | 200 | +-------------------------------------------------------+---------------------------------+---------------------------------+------------------------------------------------+ 201 | | Title | Text | Software | Citation | 202 | +=======================================================+=================================+=================================+================================================+ 203 | | Deep Convolutional Generative Adversarial Networks | `Paper `_ | `Code `_ | Rate :star2: :star2: :star2: :star2: :star2: | 204 | +-------------------------------------------------------+---------------------------------+---------------------------------+------------------------------------------------+ 205 | | Generative Adversarial Text to Image Synthesis | `Paper `_ | `Code `_ | Rate :star2: :star2: :star2: | 206 | +-------------------------------------------------------+---------------------------------+---------------------------------+------------------------------------------------+ 207 | 208 | 209 | ------------------------------------------------------------- 210 | Adversarial Autoencoders (AAE) 211 | ------------------------------------------------------------- 212 | 213 | .. _aaepaper: https://arxiv.org/abs/1511.05644 214 | .. _aaecode: https://github.com/Naresh1318/Adversarial_Autoencoder 215 | 216 | +-------------------------------------------------------+---------------------------------+---------------------------------+------------------------------------------------+ 217 | | Title | Text | Software | Citation | 218 | +=======================================================+=================================+=================================+================================================+ 219 | | Adversarial Autoencoders | `Paper `_ | `Code `_ | Rate :star2: :star2: :star2: :star2: :star2: | 220 | +-------------------------------------------------------+---------------------------------+---------------------------------+------------------------------------------------+ 221 | 222 | 223 | ------------------------------------------------------------- 224 | Generative Recurrent Adversarial Networks (GRAN) 225 | ------------------------------------------------------------- 226 | 227 | .. _granpaper: https://arxiv.org/abs/1602.05110 228 | .. _grancode: https://github.com/jiwoongim/GRAN 229 | 230 | +---------------------------------------------------------+---------------------------------+---------------------------------+------------------------------------------------+ 231 | | Title | Text | Software | Citation | 232 | +=========================================================+=================================+=================================+================================================+ 233 | | Generating images with recurrent adversarial networks | `Paper `_ | `Code `_ | Rate :star2: :star2: :star2: :star2: | 234 | +---------------------------------------------------------+---------------------------------+---------------------------------+------------------------------------------------+ 235 | 236 | 237 | ----------------------------------------------------------------- 238 | Information Maximizing Generative Adversarial Networks (InfoGan) 239 | ----------------------------------------------------------------- 240 | 241 | .. _infoganpaper: http://papers.nips.cc/paper/6399-infogan-interpretable-representation 242 | .. _infogancode: https://github.com/openai/InfoGAN 243 | 244 | +--------------------------------------------------------------------------------+---------------------------------+---------------------------------+------------------------------------------------+ 245 | | Title | Text | Software | Citation | 246 | +================================================================================+=================================+=================================+================================================+ 247 | | Infogan: Information maximizing GANs | `Paper `_ | `Code `_ | Rate :star2: :star2: :star2: :star2: :star2: | 248 | +--------------------------------------------------------------------------------+---------------------------------+---------------------------------+------------------------------------------------+ 249 | 250 | 251 | ----------------------------------------------------------------- 252 | Bidirectional Generative Adversarial Networks (BiGan) 253 | ----------------------------------------------------------------- 254 | 255 | .. _biganpaper: https://arxiv.org/abs/1605.09782 256 | .. _bigancode: https://github.com/jeffdonahue/bigan 257 | 258 | +--------------------------------------------------------------------------------+---------------------------------+---------------------------------+------------------------------------------------+ 259 | | Title | Text | Software | Citation | 260 | +================================================================================+=================================+=================================+================================================+ 261 | | Adversarial feature learning | `Paper `_ | `Code `_ | Rate :star2: :star2: :star2: :star2: :star2: | 262 | +--------------------------------------------------------------------------------+---------------------------------+---------------------------------+------------------------------------------------+ 263 | 264 | 265 | 266 | 267 | ==================== 268 | Applications 269 | ==================== 270 | 271 | 272 | -------------------------------------------- 273 | GANs Theory and Training 274 | -------------------------------------------- 275 | 276 | +------------------------------------------------------------------------------------+--------------------------------------------------------------------------+------------------------------------------------------------------------+ 277 | | Title | Text | Software | 278 | +====================================================================================+==========================================================================+========================================================================+ 279 | | Energy-based generative adversarial network | `Paper `__ | `Code `__ | 280 | +------------------------------------------------------------------------------------+---------------------------------+----------------------------------------+------------------------------------------------------------------------+ 281 | | Which Training Methods for GANs do actually Converge | `Paper `__ | `Code `__ | 282 | +------------------------------------------------------------------------------------+---------------------------------+----------------------------------------+------------------------------------------------------------------------+ 283 | | Improved Techniques for Training GANs | `Paper `__ | `Code `__ | 284 | +------------------------------------------------------------------------------------+---------------------------------+----------------------------------------+------------------------------------------------------------------------+ 285 | | Towards Principled Methods for Training Generative Adversarial Networks | `Paper `__ | | 286 | +------------------------------------------------------------------------------------+--------------------------------------------------------------------------+------------------------------------------------------------------------+ 287 | | Least Squares Generative Adversarial Networks | `Paper `__ | `Code `__ | 288 | +------------------------------------------------------------------------------------+--------------------------------------------------------------------------+------------------------------------------------------------------------+ 289 | | Wasserstein GAN | `Paper `__ | `Code `__ | 290 | +------------------------------------------------------------------------------------+--------------------------------------------------------------------------+------------------------------------------------------------------------+ 291 | | Improved Training of Wasserstein GANs | `Paper `__ | `Code `__ | 292 | +------------------------------------------------------------------------------------+--------------------------------------------------------------------------+------------------------------------------------------------------------+ 293 | | Generalization and Equilibrium in Generative Adversarial Nets | `Paper `__ | | 294 | +------------------------------------------------------------------------------------+--------------------------------------------------------------------------+------------------------------------------------------------------------+ 295 | | GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium | `Paper `__ | `Code `__ | 296 | +------------------------------------------------------------------------------------+--------------------------------------------------------------------------+------------------------------------------------------------------------+ 297 | | Spectral Normalization for Generative Adversarial Networks | `Paper `__ | `Code `__ | 298 | +------------------------------------------------------------------------------------+--------------------------------------------------------------------------+------------------------------------------------------------------------+ 299 | 300 | -------------------------------------------- 301 | Image Synthesis 302 | -------------------------------------------- 303 | 304 | +------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+------------------------------------------------------------------------+ 305 | | Title | Text | Software | 306 | +======================================================================================================+==========================================================================================+========================================================================+ 307 | | Generative Adversarial Text to Image Synthesis | `Paper `__ | `Code `__ | 308 | +------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+------------------------------------------------------------------------+ 309 | | Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space | `Paper `__ | `Code `__ | 310 | +------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+------------------------------------------------------------------------+ 311 | | Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks | `Paper `__ | `Code `__ | 312 | +------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+------------------------------------------------------------------------+ 313 | | Progressive Growing of GANs for Improved Quality, Stability, and Variation | `Paper `__ | `Code `__ | 314 | +------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+------------------------------------------------------------------------+ 315 | | StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks | `Paper `__ | `Code `__ | 316 | +------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+------------------------------------------------------------------------+ 317 | | Self-Attention Generative Adversarial Networks | `Paper `__ | `Code `__ | 318 | +------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+------------------------------------------------------------------------+ 319 | | Large Scale GAN Training for High Fidelity Natural Image Synthesis | `Paper `__ | | 320 | +------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+------------------------------------------------------------------------+ 321 | 322 | 323 | 324 | -------------------------------------------- 325 | Image-to-image translation 326 | -------------------------------------------- 327 | 328 | 329 | +------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+------------------------------------------------------------------------+ 330 | | Title | Text | Software | 331 | +======================================================================================================+==========================================================================================+========================================================================+ 332 | | Image-to-image translation using conditional adversarial nets | `Paper `__ | `Code `__ | 333 | +------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+------------------------------------------------------------------------+ 334 | | Learning to Discover Cross-Domain Relations with Generative Adversarial Networks | `Paper `__ | `Code `__ | 335 | +------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+------------------------------------------------------------------------+ 336 | | Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks | `Paper `__ | `Code `__ | 337 | +------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+------------------------------------------------------------------------+ 338 | | CoGAN: Coupled Generative Adversarial Networks | `Paper `__ | `Code `__ | 339 | +------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+------------------------------------------------------------------------+ 340 | | Unsupervised Image-to-Image Translation Networks | `Paper `__ | | 341 | +------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+------------------------------------------------------------------------+ 342 | | High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs | `Paper `__ | | 343 | +------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+------------------------------------------------------------------------+ 344 | | UNIT: UNsupervised Image-to-image Translation Networks | `Paper `__ | `Code `__ | 345 | +------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+------------------------------------------------------------------------+ 346 | | Multimodal Unsupervised Image-to-Image Translation | `Paper `__ | `Code `__ | 347 | +------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+------------------------------------------------------------------------+ 348 | 349 | 350 | -------------------------------------------- 351 | Super-resolution 352 | -------------------------------------------- 353 | 354 | +------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+------------------------------------------------------------------------+ 355 | | Title | Text | Software | 356 | +======================================================================================================+==========================================================================================+========================================================================+ 357 | | Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network | `Paper `__ | `Code `__ | 358 | +------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+------------------------------------------------------------------------+ 359 | | High-Quality Face Image Super-Resolution Using Conditional Generative Adversarial Networks | `Paper `__ | | 360 | +------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+------------------------------------------------------------------------+ 361 | | Analyzing Perception-Distortion Tradeoff using Enhanced Perceptual Super-resolution Network | `Paper `__ | `Code `__ | 362 | +------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+------------------------------------------------------------------------+ 363 | 364 | 365 | -------------------------------------------- 366 | Text to Image 367 | -------------------------------------------- 368 | 369 | +------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+------------------------------------------------------------------------+ 370 | | Title | Text | Software | 371 | +======================================================================================================+==========================================================================================+========================================================================+ 372 | | TAC-GAN – Text Conditioned Auxiliary Classifier Generative Adversarial Network | `Paper `__ | `Code `__ | 373 | +------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+------------------------------------------------------------------------+ 374 | | Generative Adversarial Text to Image Synthesis | `Paper `__ | `Code `__ | 375 | +------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+------------------------------------------------------------------------+ 376 | | Learning What and Where to Draw | `Paper `__ | `Code `__ | 377 | +------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+------------------------------------------------------------------------+ 378 | 379 | 380 | -------------------------------------------- 381 | Image Editing 382 | -------------------------------------------- 383 | 384 | 385 | +------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+------------------------------------------------------------------------+ 386 | | Title | Text | Software | 387 | +======================================================================================================+==========================================================================================+========================================================================+ 388 | | Invertible Conditional GANs for image editing | `Paper `__ | `Code `__ | 389 | +------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+------------------------------------------------------------------------+ 390 | | Image De-raining Using a Conditional Generative Adversarial Network | `Paper `__ | `Code `__ | 391 | +------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+------------------------------------------------------------------------+ 392 | 393 | -------------------------------------------- 394 | ETC 395 | -------------------------------------------- 396 | 397 | +------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+------------------------------------------------------------------------+ 398 | | Title | Text | Software | 399 | +======================================================================================================+==========================================================================================+========================================================================+ 400 | | Generating multi-label discrete patient records using generative adversarial networks | `Paper `__ | `Code `__ | 401 | +------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+------------------------------------------------------------------------+ 402 | | Adversarial Generation of Natural Language | `Paper `__ | | 403 | +------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+------------------------------------------------------------------------+ 404 | | Language Generation with Recurrent Generative Adversarial Networks without Pre-training | `Paper `__ | `Code `__ | 405 | +------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+------------------------------------------------------------------------+ 406 | | Adversarial ranking for language generation | `Paper `__ | `Code `__ | 407 | +------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+------------------------------------------------------------------------+ 408 | | Adversarial Training Methods for Semi-Supervised Text Classification | `Paper `__ | `Code `__ | 409 | +------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------+------------------------------------------------------------------------+ 410 | 411 | 412 | 413 | ************ 414 | Courses 415 | ************ 416 | 417 | .. image:: _img/mainpage/online.png 418 | 419 | * **Deep Learning: GANs and Variational Autoencoders** by Udemy: 420 | [`Link `_] 421 | 422 | * **Differentiable Inference and Generative Models** by the University of Toronto: 423 | [`Link `_] 424 | 425 | * **Learning Generative Adversarial Networks** by Udemy: 426 | [`Link `_] 427 | 428 | 429 | ************ 430 | Books 431 | ************ 432 | 433 | .. image:: _img/mainpage/books.jpg 434 | 435 | * **GANs in Action - Deep learning with Generative Adversarial Networks** by manning Publications: 436 | [`Link `_] 437 | 438 | 439 | ************ 440 | Tutorials 441 | ************ 442 | 443 | .. image:: _img/mainpage/tutorial.png 444 | 445 | * **GANs from Scratch 1: A deep introduction. With code in PyTorch and TensorFlow**: 446 | [`Link `_] 447 | 448 | * **Keep Calm and train a GAN. Pitfalls and Tips on training Generative Adversarial Networks**: 449 | [`Link `_] 450 | 451 | * **CVPR 2018 Tutorial on GANs**: 452 | [`Link `_] 453 | 454 | * **Introductory guide to Generative Adversarial Networks (GANs) and their promise!**: 455 | [`Link `_] 456 | 457 | * **Generative Adversarial Networks for beginners**: 458 | [`Link `_] 459 | 460 | * **Understanding and building Generative Adversarial Networks(GANs)**: 461 | [`Link `_] 462 | 463 | ************************ 464 | Pull Request Process 465 | ************************ 466 | 467 | Please consider the following criterions in order to help us in a better way: 468 | 469 | 1. The pull request is mainly expected to be a link suggestion. 470 | 2. Please make sure your suggested resources are not obsolete or broken. 471 | 3. Ensure any install or build dependencies are removed before the end of the layer when doing a 472 | build and creating a pull request. 473 | 4. Add comments with details of changes to the interface, this includes new environment 474 | variables, exposed ports, useful file locations and container parameters. 475 | 5. You may merge the Pull Request in once you have the sign-off of at least one other developer, or if you 476 | do not have permission to do that, you may request the owner to merge it for you if you believe all checks are passed. 477 | 478 | ************************ 479 | Final Note 480 | ************************ 481 | 482 | We are looking forward to your kind feedback. Please help us to improve this open source project and make our work better. 483 | For contribution, please create a pull request and we will investigate it promptly. Once again, we appreciate 484 | your kind feedback and support. 485 | -------------------------------------------------------------------------------- /_img/mainpage/Newton_optimization_vs_grad_descent.svg: -------------------------------------------------------------------------------- 1 | 2 | 20 | 22 | 23 | 25 | image/svg+xml 26 | 28 | 29 | 30 | 31 | 33 | 40 | 45 | 46 | 53 | 58 | 59 | 66 | 71 | 72 | 73 | 89 | Matlab Figure Converted by PLOT2SVG written by Juerg Schwizer 91 | 94 | 96 | 102 | 103 | 105 | 108 | 112 | 114 | 115 | 118 | 122 | 124 | 125 | 128 | 132 | 134 | 135 | 138 | 142 | 144 | 145 | 148 | 152 | 154 | 155 | 158 | 162 | 164 | 165 | 168 | 172 | 174 | 175 | 178 | 180 | 184 | 188 | 192 | 196 | 197 | 198 | 201 | 203 | 207 | 211 | 215 | 219 | 220 | 221 | 224 | 228 | 230 | 231 | 234 | 238 | 240 | 245 | 250 | 251 | 254 | 258 | 259 | 262 | 266 | 267 | x 279 | x 291 | 0 303 | 304 | 305 | 306 | -------------------------------------------------------------------------------- /_img/mainpage/RNN2.svg: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 19 | 21 | 29 | 35 | 36 | 44 | 50 | 51 | 59 | 65 | 66 | 75 | 81 | 82 | 91 | 97 | 98 | 106 | 112 | 113 | 122 | 128 | 129 | 138 | 144 | 145 | 153 | 159 | 160 | 168 | 174 | 175 | 183 | 189 | 190 | 198 | 204 | 205 | 214 | 220 | 221 | 229 | 235 | 236 | 237 | 257 | 259 | 260 | 262 | image/svg+xml 263 | 265 | 266 | 267 | 268 | 269 | 274 | 277 | 280 | 283 | 286 | 295 | 301 | 307 | 313 | 319 | xt-1 333 | ht-1 347 | ot-1 361 | 366 | 371 | 380 | 386 | xt 400 | 406 | 413 | ot 427 | 433 | 442 | ht+1 456 | 461 | 466 | 472 | 478 | 484 | 490 | xt+1 504 | ot+1 518 | 524 | 530 | GRU unit 542 | 548 | 554 | 555 | 558 | 567 | σ 579 | 582 | 591 | tanh 603 | 604 | 607 | 615 | 620 | 625 | 626 | 634 | 639 | 644 | 650 | 656 | 662 | 667 | 673 | 679 | 685 | 691 | ht-1 705 | xt 719 | ht 733 | Rt 747 | ht 761 | 767 | 773 | 782 | σ 794 | 800 | 808 | 813 | 818 | 821 | 824 | 832 | 1- 844 | 845 | 846 | 849 | 857 | 862 | 867 | 868 | 874 | 880 | Zt 894 | 900 | 906 | 912 | 918 | 924 | 930 | 936 | 942 | 948 | 956 | 964 | 972 | 973 | ... 986 | ... 1000 | 1001 | 1002 | 1003 | --------------------------------------------------------------------------------