├── AllGAN-r2.tsv ├── INDEX.j2.md ├── LICENSE ├── README-one.md ├── README.j2.md ├── README.md ├── docs ├── AllGan.json ├── default.min.css ├── highlight.min.js ├── index.html ├── png │ ├── forkme.png │ ├── forkme_right_darkblue_121621.png │ ├── wordcloud_abbr.png │ ├── wordcloud_category.png │ └── wordcloud_title.png ├── search.html └── wp.min.css ├── update.py ├── wordcloud_abbr.png ├── wordcloud_category.png └── wordcloud_title.png /INDEX.j2.md: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 13 | 14 | 15 | 16 | 17 | 19 | 20 | 21 | All about the GANs(Generative Adversarial Networks) - Summarized lists for GAN 22 | 23 | 24 | 25 | Fork me on GitHub 26 | 27 | 28 | 29 | 51 | 52 | 53 |
54 | 55 |

What is GANs?

56 |

GANs(Generative Adversarial Networks) are the models that used in unsupervised machine learning, implemented by a system of two neural networks competing against each other in a zero-sum game framework. It was introduced by Ian Goodfellow et al. in 2014.

57 | 58 |

concept diagram of GAN model. Credit: O’Reilly (Credit: O’Reilly)

59 | 60 | 61 | "The most important one, in my opinion, is adversarial training (also called GAN for Generative Adversarial Networks). This is an idea that was originally proposed by Ian Goodfellow when he was a student with Yoshua Bengio at the University of Montreal (he since moved to Google Brain and recently to OpenAI). 62 | This, and the variations that are now being proposed is the most interesting idea in the last 10 years in ML, in my opinion." 63 | (Facebook’s AI research director Yann LeCun) 64 | 65 | 66 |
67 | 68 | 69 | 70 | 71 |

What is this list?

72 |

The purpose of this repository is providing the curated list of the state-of-the-art works on the field of Generative Adversarial Networks since their introduction in 2014.

73 | 74 |

Title Word Cloud of GAN papers
75 | (Word cloud of Title)

76 | 77 |

Category Word Cloud of GAN papers
(Word cloud of Category)

78 | 79 |

Abbr. Name Word Cloud of GAN papers
(Word cloud of Abbr. name)

80 | 81 |

This list provides a curated list that merged information from various GAN lists and repositories as below:

82 |

Reference repositories

83 | 97 | 98 |

99 |

You can also check out the same data in a tabular format with functionality to filter by year or do a quick search by title here.

100 |

Contributions for this repository are always welcome!!

101 |

Please contact me at hollobit@etri.re.kr or send a pull request. You can have to add links through pull requests or create an issue which something I missed or need to start a discussion.

102 |
103 | 104 |

All GANs

105 | 106 | 107 | 108 | 109 | {% set ncount = {'value': 1} %} 110 |

What's new

111 |
112 | {% for gan in gans if 'New' in gan['Category'] %} 113 |
{{ gan['Title'] }} (No: {{ gan['Mnum']}})
114 |
[Search] [Scholar] 115 | {%- if gan['pdf'] != '-' and gan['pdf'] != '' -%} [PDF] {% endif %} 116 | {%- if ncount.update({'value': (ncount.value + 1)}) -%} {% endif %} 117 | {%- if gan['Arxiv'] != '-' and gan['Arxiv'] != '' -%} [arXiv] {% endif %} 118 | {%- if gan['Official_Code'] != '-' and gan['Official_Code'] != '' -%} [github] {% endif %} 119 | {%- if gan['Tensorflow'] != '-' and gan['Tensorflow'] != '' -%} [TensorFlow] {% endif %} 120 | {%- if gan['PyTorch'] != '-' and gan['PyTorch'] != '' -%} [PyTorch] {% endif %} 121 | {%- if gan['KERAS'] != '-' and gan['KERAS'] != '' -%} [KERAS] {% endif %} 122 | {%- if gan['Web'] != '-' and gan['Web'] != '' -%} [Web] {% endif %} 123 | 124 | - {%- if gan['Citations'] | int > 50 %} :dart: {% endif %} 125 | {%- if gan['Stars'] | int > 10 %} :octocat: {% endif %} `{{ gan['Year'] }}/{{ gan['Month'] }}` {# #} 126 | {%- if gan['Medical'] != '-' -%} `Medical: {{ gan['Medical'] }}` {% endif %} 127 | {%- if gan['Category'] != '-' -%} `{{ gan['Category'] }}` {% endif %} 128 | {%- if gan['Abbr.'] != '-' and gan['Abbr.'] != '' %} `{{ gan['Abbr.'] }}` {% endif %} 129 | {%- if gan['Citations'] != '0' and gan['Citations'] != '' %} `Citation: {{ gan['Citations'] }}` {% endif %} 130 | {%- if gan['Stars'] != '-' and gan['Stars'] != '' %} `Stars: {{ gan['Stars'] }}` {% endif %} 131 |
132 | 133 | {% endfor %} 134 |
135 |
136 | 137 | {% set count = {'value': 1} %} 138 | {% set syear_list = [2018, 2017, 2016, 2015, 2014] %} 139 | {% for syear in syear_list %} 140 |

{{ syear }}

141 |
142 | {% for gan in gans if gan['Year']|int == syear %} 143 |
{{ gan['Title'] }} (No: {{ gan['Mnum']}})
144 |
[Search] [Scholar] 145 | {%- if gan['pdf'] != '-' and gan['pdf'] != '' -%} [PDF] {% endif %} 146 | {%- if count.update({'value': (count.value + 1)}) -%} {% endif %} 147 | {%- if gan['Arxiv'] != '-' and gan['Arxiv'] != '' -%} [arXiv] {% endif %} 148 | {%- if gan['Official_Code'] != '-' and gan['Official_Code'] != '' -%} [github] {% endif %} 149 | {%- if gan['Tensorflow'] != '-' and gan['Tensorflow'] != '' -%} [TensorFlow] {% endif %} 150 | {%- if gan['PyTorch'] != '-' and gan['PyTorch'] != '' -%} [PyTorch] {% endif %} 151 | {%- if gan['KERAS'] != '-' and gan['KERAS'] != '' -%} [KERAS] {% endif %} 152 | {%- if gan['Web'] != '-' and gan['Web'] != '' -%} [Web] {% endif %} 153 | 154 | - {%- if gan['Citations'] | int > 50 %} :dart: {% endif %} 155 | {%- if gan['Stars'] | int > 10 %} :octocat: {% endif %} `{{ gan['Year'] }}/{{ gan['Month'] }}` {# #} 156 | {%- if gan['Medical'] != '-' -%} `Medical: {{ gan['Medical'] }}` {% endif %} 157 | {%- if gan['Category'] != '-' -%} `{{ gan['Category'] }}` {% endif %} 158 | {%- if gan['Abbr.'] != '-' and gan['Abbr.'] != '' %} `{{ gan['Abbr.'] }}` {% endif %} 159 | {%- if gan['Citations'] != '0' and gan['Citations'] != '' %} `Citation: {{ gan['Citations'] }}` {% endif %} 160 | {%- if gan['Stars'] != '-' and gan['Stars'] != '' %} `Stars: {{ gan['Stars'] }}` {% endif %} 161 |
162 | {% endfor %} 163 |
164 | {% endfor %} 165 | 166 | 167 |
168 | 169 | {% set mcount = {'value': 1} %} 170 |

Medical

171 |
172 | {% for gan in gans if gan['Medical'] != '-' %} 173 |
{{ gan['Title'] }} (No: {{ gan['Mnum']}})
174 |
[Search] [Scholar] 175 | {%- if gan['pdf'] != '-' and gan['pdf'] != '' -%} [PDF] {% endif %} 176 | {%- if mcount.update({'value': (mcount.value + 1)}) -%} {% endif %} 177 | {%- if gan['Arxiv'] != '-' and gan['Arxiv'] != '' -%} [arXiv] {% endif %} 178 | {%- if gan['Official_Code'] != '-' and gan['Official_Code'] != '' -%} [github] {% endif %} 179 | {%- if gan['Tensorflow'] != '-' and gan['Tensorflow'] != '' -%} [TensorFlow] {% endif %} 180 | {%- if gan['PyTorch'] != '-' and gan['PyTorch'] != '' -%} [PyTorch] {% endif %} 181 | {%- if gan['KERAS'] != '-' and gan['KERAS'] != '' -%} [KERAS] {% endif %} 182 | {%- if gan['Web'] != '-' and gan['Web'] != '' -%} [Web] {% endif %} 183 | 184 | - {%- if gan['Citations'] | int > 50 %} :dart: {% endif %} 185 | {%- if gan['Stars'] | int > 10 %} :octocat: {% endif %} `{{ gan['Year'] }}/{{ gan['Month'] }}` {# #} 186 | {%- if gan['Medical'] != '-' -%} `Medical: {{ gan['Medical'] }}` {% endif %} 187 | {%- if gan['Category'] != '-' -%} `{{ gan['Category'] }}` {% endif %} 188 | {%- if gan['Abbr.'] != '-' and gan['Abbr.'] != '' %} `{{ gan['Abbr.'] }}` {% endif %} 189 | {%- if gan['Citations'] != '0' and gan['Citations'] != '' %} `Citation: {{ gan['Citations'] }}` {% endif %} 190 | {%- if gan['Stars'] != '-' and gan['Stars'] != '' %} `Stars: {{ gan['Stars'] }}` {% endif %} 191 |
192 | 193 | {% endfor %} 194 |
195 | 196 |
197 | 198 | {% set aucount = {'value': 1} %} 199 |

Augmentation

200 |
201 | {% for gan in gans if 'Augmentation' in gan['Category'] %} 202 |
{{ gan['Title'] }} (No: {{ gan['Mnum']}})
203 |
[Search] [Scholar] 204 | {%- if gan['pdf'] != '-' and gan['pdf'] != '' -%} [PDF] {% endif %} 205 | {%- if aucount.update({'value': (aucount.value + 1)}) -%} {% endif %} 206 | {%- if gan['Arxiv'] != '-' and gan['Arxiv'] != '' -%} [arXiv] {% endif %} 207 | {%- if gan['Official_Code'] != '-' and gan['Official_Code'] != '' -%} [github] {% endif %} 208 | {%- if gan['Tensorflow'] != '-' and gan['Tensorflow'] != '' -%} [TensorFlow] {% endif %} 209 | {%- if gan['PyTorch'] != '-' and gan['PyTorch'] != '' -%} [PyTorch] {% endif %} 210 | {%- if gan['KERAS'] != '-' and gan['KERAS'] != '' -%} [KERAS] {% endif %} 211 | {%- if gan['Web'] != '-' and gan['Web'] != '' -%} [Web] {% endif %} 212 | 213 | - {%- if gan['Citations'] | int > 50 %} :dart: {% endif %} 214 | {%- if gan['Stars'] | int > 10 %} :octocat: {% endif %} `{{ gan['Year'] }}/{{ gan['Month'] }}` {# #} 215 | {%- if gan['Medical'] != '-' -%} `Medical: {{ gan['Medical'] }}` {% endif %} 216 | {%- if gan['Category'] != '-' -%} `{{ gan['Category'] }}` {% endif %} 217 | {%- if gan['Abbr.'] != '-' and gan['Abbr.'] != '' %} `{{ gan['Abbr.'] }}` {% endif %} 218 | {%- if gan['Citations'] != '0' and gan['Citations'] != '' %} `Citation: {{ gan['Citations'] }}` {% endif %} 219 | {%- if gan['Stars'] != '-' and gan['Stars'] != '' %} `Stars: {{ gan['Stars'] }}` {% endif %} 220 |
221 | 222 | {% endfor %} 223 |
224 | 225 |
226 | 227 |

#### GANs counter: {{ count.value-1 }}

228 | 229 |

#### Added new papers: {{ ncount.value-1 }}

230 | 231 |

#### Medical related papers: {{ mcount.value-1 }}

232 | 233 |

#### Augmentation topic papers: {{ aucount.value-1 }}

234 | 235 |

#### Modified: {{ nowts.strftime('%A, %b %d %Y / %X') }}

236 | 237 |

MIT (c) 2017, 2018 Jonathan Jeon (hollobit_at_etri.re.kr)

238 | 239 | 240 | 241 | 242 |
243 | 244 | 245 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2017 Jonghong Jeon (Jonathan Jeon) 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 | -------------------------------------------------------------------------------- /README.j2.md: -------------------------------------------------------------------------------- 1 | # All-About-the-GAN 2 | 3 | ### GAN(Generative Adversarial Networks) are the models that used in unsupervised machine learning, implemented by a system of two neural networks competing against each other in a zero-sum game framework. It was introduced by Ian Goodfellow et al. in 2014. 4 | 5 | The purpose of this repository is providing the curated list of the state-of-the-art works on the field of Generative Adversarial Networks since their introduction in 2014. 6 | 7 |


(Word Cloud of Title)

8 | 9 |


(Word Cloud of Title)

10 | 11 |

Abbr. Name Word Cloud of GAN papers
(Word cloud of Abbr. name)

12 | 13 | It provides a list that merged information from various GAN lists and repositories as below: 14 | 15 | ### :link: Reference repositories 16 | * [[GAN zoo]](https://github.com/hindupuravinash/the-gan-zoo) - A list of all named GANs! by hindupuravinash 17 | * Delving deep into Generative Adversarial Networks (GANs) [[Delving]](https://github.com/GKalliatakis/Delving-deep-into-GANs) by GKalliatakis 18 | * Awesome GAN for Medical Imaging [[Medical]](https://github.com/xinario/awesome-gan-for-medical-imaging/) by xinario 19 | * [[Adversarial Nets Papers]](https://github.com/zhangqianhui/AdversarialNetsPapers/) The classic about Generative Adversarial Networks 20 | * [[Really Awesome GAN]](https://github.com/nightrome/really-awesome-gan) by nightrome 21 | * [[GANs Paper Collection]](https://github.com/shawnyuen/GANsPaperCollection) by shawnyuen 22 | * [[GAN awesome applications]](https://github.com/nashory/gans-awesome-applications) by nashory 23 | * [[GAN timeline]](https://github.com/dongb5/GAN-Timeline) by dongb5 24 | * [[GAN comparison without cherry-picking]](https://github.com/khanrc/tf.gans-comparison) by khanrc 25 | * Collection of generative models in [[Keras]](https://github.com/eriklindernoren/Keras-GAN), [[Pytorch version]](https://github.com/znxlwm/pytorch-generative-model-collections), [[Tensorflow version]](https://github.com/hwalsuklee/tensorflow-generative-model-collections), [[Chainer version]](https://github.com/pfnet-research/chainer-gan-lib) 26 | * [[Tensor layer]](https://github.com/tensorlayer/tensorlayer) 27 | * [[Tensor pack]](https://github.com/ppwwyyxx/tensorpack) 28 | ---- 29 | 30 | You can also check out the same data in a tabular format with functionality to filter by year or do a quick search by title [here](https://github.com/hollobit/All-About-the-GAN/blob/master/AllGAN-r2.tsv). 31 | 32 | Contributions are welcome. Please contact me at hollobit@etri.re.kr or send a pull request. You can have to add links through pull requests or create an issue which something I missed or need to start a discussion. 33 | 34 | ---- 35 | 36 | {% set count = {'value': 1} %} 37 | {% for gan in gans %} 38 | {{count.value}}. __{{ gan['Title'] }}__ - ([Search](http://www.google.com/search?q={{ gan['Title']|urlencode() }})) ([Scholar](http://scholar.google.com/scholar?q={{ gan['Title']|urlencode() }})) ([PDF]({{ gan['pdf'] }})) 39 | {%- if count.update({'value': (count.value + 1)}) -%} {% endif %} 40 | {%- if gan['Arxiv'] != '-' and gan['Arxiv'] != '' -%} ([arXiv]({{ gan['Arxiv'] }})) {% endif %} 41 | {%- if gan['Official_Code'] != '-' and gan['Official_Code'] != '' -%} ([github]({{ gan['Official_Code'] }})) {% endif %} 42 | {%- if gan['Tensorflow'] != '-' and gan['Tensorflow'] != '' -%} ([TensorFlow]({{ gan['Tensorflow'] }})) {% endif %} 43 | {%- if gan['PyTorch'] != '-' and gan['PyTorch'] != '' -%} ([PyTorch]({{ gan['PyTorch'] }})) {% endif %} 44 | {%- if gan['KERAS'] != '-' and gan['KERAS'] != '' -%} ([KERAS]({{ gan['KERAS'] }})) {% endif %} 45 | {%- if gan['Web'] != '-' and gan['Web'] != '' -%} ([Web]({{ gan['Web'] }})) {% endif %} 46 | 47 | - {%- if gan['Citations'] | int > 50 %} :dart: {% endif %} 48 | {%- if gan['Stars'] | int > 10 %} :octocat: {% endif %} `{{ gan['Year'] }}/{{ gan['Month'] }}` {# #} 49 | {%- if gan['Medical'] != '-' -%} __`Medical: {{ gan['Medical'] }}`__ {% endif %} 50 | {%- if gan['Category'] != '-' -%} `{{ gan['Category'] }}` {% endif %} 51 | {%- if gan['Abbr.'] != '-' and gan['Abbr.'] != '' %} __`{{ gan['Abbr.'] }}`__ {% endif %} 52 | {%- if gan['Citations'] != '0' and gan['Citations'] != '' %} `Citation: {{ gan['Citations'] }}` {% endif %} 53 | {%- if gan['Stars'] != '-' and gan['Stars'] != '' %} `Stars: {{ gan['Stars'] }}` {% endif %} 54 | 55 | 56 | {% endfor %} 57 | 58 | ---- 59 | 60 | #### GAN counter: {{ count.value-1 }} 61 | 62 | #### Modified: {{ nowts.strftime('%A, %b %d %Y / %X') }} 63 | 64 | MIT (c) 2017, 2018 Jonathan Jeon 65 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # All-About-the-GANs 2 | 3 | ### GAN(Generative Adversarial Networks)s are the models that used in unsupervised machine learning, implemented by a system of two neural networks competing against each other in a zero-sum game framework. It was introduced by Ian Goodfellow et al. in 2014. 4 | 5 | The purpose of this repository is providing the curated list of the state-of-the-art works on the field of Generative Adversarial Networks since their introduction in 2014. 6 | 7 |


(Word Cloud of Title)

8 | 9 |


(Word Cloud of Category)

10 | 11 |

Abbr. Name Word Cloud of GAN papers
(Word cloud of Abbr. name)

12 | 13 | It provides a list that merged information from various GAN lists and repositories as below: 14 | 15 | ### :link: Reference repositories 16 | * [[GAN zoo]](https://github.com/hindupuravinash/the-gan-zoo) - A list of all named GANs! by hindupuravinash 17 | * Delving deep into Generative Adversarial Networks (GANs) [[Delving]](https://github.com/GKalliatakis/Delving-deep-into-GANs) by GKalliatakis 18 | * Awesome GAN for Medical Imaging [[Medical]](https://github.com/xinario/awesome-gan-for-medical-imaging/) by xinario 19 | * [[Adversarial Nets Papers]](https://github.com/zhangqianhui/AdversarialNetsPapers/) The classic about Generative Adversarial Networks 20 | * [[Really Awesome GAN]](https://github.com/nightrome/really-awesome-gan) by nightrome 21 | * [[GANs Paper Collection]](https://github.com/shawnyuen/GANsPaperCollection) by shawnyuen 22 | * [[GAN awesome applications]](https://github.com/nashory/gans-awesome-applications) by nashory 23 | * [[GAN timeline]](https://github.com/dongb5/GAN-Timeline) by dongb5 24 | * [[GAN comparison without cherry-picking]](https://github.com/khanrc/tf.gans-comparison) by khanrc 25 | * Collection of generative models in [[Keras]](https://github.com/eriklindernoren/Keras-GAN), [[Pytorch version]](https://github.com/znxlwm/pytorch-generative-model-collections), [[Tensorflow version]](https://github.com/hwalsuklee/tensorflow-generative-model-collections), [[Chainer version]](https://github.com/pfnet-research/chainer-gan-lib) 26 | * [[Tensor layer]](https://github.com/tensorlayer/tensorlayer) 27 | * [[Tensor pack]](https://github.com/ppwwyyxx/tensorpack) 28 | ---- 29 | 30 | You can also check out the same data in a tabular format with functionality to filter by year or do a quick search by title [here](https://github.com/hollobit/All-About-the-GAN/blob/master/AllGAN-r2.tsv). 31 | 32 | Contributions are welcome. Please contact me at hollobit@etri.re.kr or send a pull request. You can have to add links through pull requests or create an issue which something I missed or need to start a discussion. 33 | 34 | ---- 35 | ## You can access the whole list by two way 36 | ### 1. [simple web style - https://hollobit.github.io/All-About-the-GAN/](https://hollobit.github.io/All-About-the-GAN/) 37 | ### 2. [Readme style](https://github.com/hollobit/All-About-the-GAN/blob/master/README-one.md) 38 | ### 3. 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1240px){main{width:calc(100% - 462px)}}@media screen and (max-width: 1140px){main{width:calc(100% - 220px)}}@media screen and (max-width: 800px){main{width:calc(100% - 110px)}}@media screen and (max-width: 1140px){aside{transform:translateX(-105%);box-shadow:0 2px 0 0 rgba(0,0,0,0.16),0 2px 20px 0 rgba(0,0,0,0.6);transition:box-shadow .3s,transform .3s}}@media screen and (max-width: 900px){aside{width:80%}}@media screen and (max-width: 1140px){#menu ~ label#open{display:block}#menu:checked ~ main>label#exit{display:block}#menu:checked ~ aside{transform:translateX(0)}}code{font-size:0.9em;font-family:'Courier', monospace;font-style:normal;line-height:1em;display:inline-block;border-radius:3px;padding:3px 6px;background-color:#f2f2f2;color:#000}code.tag:before{content:'<'}code.tag:after{content:'>'}pre{overflow-x:scroll;border-radius:3px;margin:10px 0;display:block;width:auto}pre>code{min-width:100%;font-size:0.9em;font-family:'Courier', monospace;font-style:normal;line-height:1.5em;background-color:#f2f2f2;color:#000}div.card{padding:10px;margin-bottom:10px;border:1px solid #e6e6e6} 2 | -------------------------------------------------------------------------------- /update.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ Update Readme.md and cumulative_gans.jpg """ 3 | from __future__ import print_function 4 | from __future__ import division 5 | from wordcloud import WordCloud 6 | from wordcloud import STOPWORDS 7 | 8 | import numpy as np 9 | import matplotlib.pyplot as plt 10 | import sys 11 | import datetime 12 | import pandas as pd 13 | import csv 14 | import json 15 | import os 16 | 17 | def load_data(): 18 | """ Load GANs data from the AllGAN.csv file """ 19 | import csv 20 | import codecs 21 | 22 | with codecs.open('AllGAN-r2.tsv',"rbU", "utf-8") as fid: 23 | reader = csv.DictReader(fid, delimiter='\t') 24 | gans = [row for row in reader] 25 | return gans 26 | 27 | 28 | def update_readme(gans): 29 | """ Update the Readme.md text file from a Jinja2 template """ 30 | import jinja2 as j2 31 | 32 | gans.sort(key=lambda v: v['Title'].upper()) 33 | j2_env = j2.Environment(loader=j2.FileSystemLoader('.'), 34 | trim_blocks=True, lstrip_blocks=True) 35 | 36 | j2_env.globals['nowts'] = datetime.datetime.now() 37 | 38 | with open('README-one.md', 'w') as fid: 39 | print(j2_env.get_template('README.j2.md').render(gans=gans), file=fid) 40 | 41 | def update_index(gans): 42 | """ Update the index.html text file from a Jinja2 template """ 43 | import jinja2 as j2 44 | 45 | try: 46 | gans.sort(key=lambda v: ((int(v['Year']) if v['Year'].isdigit() else v['Year']) 47 | , (int(v['Month']) if v['Month'].isdigit() else v['Month'])), reverse=True) 48 | except: 49 | pass 50 | j2_env = j2.Environment(loader=j2.FileSystemLoader('.'), 51 | trim_blocks=True, lstrip_blocks=True) 52 | 53 | j2_env.globals['nowts'] = datetime.datetime.now() 54 | 55 | with open('docs/index.html', 'w') as fid: 56 | print(j2_env.get_template('INDEX.j2.md').render(gans=gans), file=fid) 57 | 58 | 59 | def update_figure(gans): 60 | """ Update the figure cumulative_gans.jpg """ 61 | data = np.array([int(gan['Year']) + int(gan['Month']) / 12 62 | for gan in gans]) 63 | x_range = int(np.ceil(np.max(data) - np.min(data)) * 12) + 1 64 | y_range = int(np.ceil(data.size / 10)) * 10 + 1 65 | 66 | with plt.style.context("seaborn"): 67 | plt.hist(data, x_range, cumulative="True") 68 | plt.xticks(range(2014, 2018)) 69 | plt.yticks(np.arange(0, y_range, 15)) 70 | plt.title("Cumulative number of named GAN papers by month") 71 | plt.xlabel("Year") 72 | plt.ylabel("Total number of papers") 73 | plt.savefig('cumulative_gans.jpg') 74 | 75 | def update_wordcloud_title(): 76 | """ Update the figure wordcloud_title.jpg """ 77 | 78 | data = pd.read_csv('AllGAN-r2.tsv',delimiter='\t', encoding='utf-8') 79 | 80 | # tmp_data = data['Title'].split(" ") for x in data 81 | 82 | # count_list = list([list(x) for x in data['Title'].value_counts().reset_index().values]) 83 | 84 | # wordcloud = WordCloud(stopwords=STOPWORDS,relative_scaling = 0.2, 85 | # max_words=2000, background_color='white').generate_from_frequencies(tmp_data) 86 | stopwords = set(STOPWORDS) 87 | #ganstop = ['Generative','Adversarial', 'Networks', 'Network', 'GAN', 'GANs', 'using', 'Learning', 'Training', 'Generation', 88 | # 'Neural', 'Net', 'Model', 'Nets', 'Deep', 'Based', 'Via', 'Conditional', 'Models', 'Examples'] 89 | #stopwords.add(ganstop) 90 | 91 | stopwords.add('Generative') 92 | stopwords.add('Adversarial') 93 | stopwords.add('Networks') 94 | stopwords.add('Network') 95 | stopwords.add('GAN') 96 | stopwords.add('GANs') 97 | stopwords.add('using') 98 | stopwords.add('Learning') 99 | stopwords.add('Training') 100 | stopwords.add('Generation') 101 | stopwords.add('Neural') 102 | stopwords.add('Net') 103 | stopwords.add('Model') 104 | stopwords.add('Nets') 105 | stopwords.add('Deep') 106 | stopwords.add('Based') 107 | stopwords.add('Via') 108 | stopwords.add('Conditional') 109 | stopwords.add('Models') 110 | stopwords.add('Examples') 111 | 112 | wordcloud = WordCloud(stopwords=stopwords,relative_scaling = 0.2, random_state=3, 113 | max_words=2000, background_color='white').generate(' '.join(data['Title'])) 114 | 115 | plt.figure(figsize=(12,12)) 116 | plt.imshow(wordcloud, interpolation="bilinear") 117 | plt.axis("off") 118 | #plt.show() 119 | #plt.savefig('wordcloud_title.png') 120 | wordcloud.to_file('wordcloud_title.png') 121 | wordcloud.to_file('docs/png/wordcloud_title.png') 122 | 123 | def update_wordcloud_category(): 124 | """ Update the figure wordcloud_category.jpg """ 125 | 126 | data = pd.read_csv('AllGAN-r2.tsv',delimiter='\t', encoding='utf-8') 127 | 128 | wordcloud = WordCloud(stopwords=STOPWORDS,relative_scaling = 0.2, random_state=3, 129 | max_words=2000, background_color='white').generate(' '.join(data['Category'])) 130 | 131 | plt.figure(figsize=(12,12)) 132 | plt.imshow(wordcloud, interpolation="bilinear") 133 | plt.axis("off") 134 | #plt.show() 135 | #plt.savefig('wordcloud_title.png') 136 | wordcloud.to_file('wordcloud_category.png') 137 | wordcloud.to_file('docs/png/wordcloud_category.png') 138 | 139 | def update_wordcloud_abbr(): 140 | """ Update the figure wordcloud_category.jpg """ 141 | 142 | data = pd.read_csv('AllGAN-r2.tsv',delimiter='\t', encoding='utf-8') 143 | 144 | wordcloud = WordCloud(stopwords=STOPWORDS,relative_scaling = 0.2, random_state=3, 145 | max_words=2000, background_color='white').generate(' '.join(data['Abbr.'])) 146 | 147 | plt.figure(figsize=(12,12)) 148 | plt.imshow(wordcloud, interpolation="bilinear") 149 | plt.axis("off") 150 | #plt.show() 151 | #plt.savefig('wordcloud_title.png') 152 | wordcloud.to_file('wordcloud_abbr.png') 153 | wordcloud.to_file('docs/png/wordcloud_abbr.png') 154 | 155 | def update_csv2json(): 156 | 157 | # COLUMNS = ('Mnum','Abbr.','Title','Year','Month','Citations','pdf','Arxiv','Official_Code','Tensorflow','PyTorch','KERAS', 'Stars','Web','No','SN','Medical','Category') 158 | with open('AllGan-r2.tsv', 'r') as f: 159 | reader = csv.DictReader(f, delimiter='\t') 160 | rows = list(reader) 161 | 162 | with open('docs/AllGan.json', 'w') as f: 163 | f.write(json.dumps(rows, sort_keys=False, separators=(',', ': '), ensure_ascii=False, indent=4)) 164 | 165 | if __name__ == '__main__': 166 | try: 167 | reload(sys) # Python 2 168 | sys.setdefaultencoding('utf-8') 169 | except NameError: 170 | pass # Python 3 171 | 172 | GANS = load_data() 173 | update_wordcloud_title() 174 | update_wordcloud_category() 175 | update_wordcloud_abbr() 176 | update_readme(GANS) 177 | update_index(GANS) 178 | update_csv2json() 179 | # update_figure(GANS) 180 | -------------------------------------------------------------------------------- /wordcloud_abbr.png: 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