├── wordcloud_abbr.png
├── docs
├── png
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│ ├── wordcloud_abbr.png
│ ├── wordcloud_title.png
│ ├── wordcloud_category.png
│ └── forkme_right_darkblue_121621.png
├── default.min.css
├── search.html
├── wp.min.css
└── highlight.min.js
├── wordcloud_title.png
├── wordcloud_category.png
├── LICENSE
├── README.md
├── README.j2.md
├── update.py
└── INDEX.j2.md
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1 | .hljs{display:block;overflow-x:auto;padding:0.5em;background:#F0F0F0}.hljs,.hljs-subst{color:#444}.hljs-comment{color:#888888}.hljs-keyword,.hljs-attribute,.hljs-selector-tag,.hljs-meta-keyword,.hljs-doctag,.hljs-name{font-weight:bold}.hljs-type,.hljs-string,.hljs-number,.hljs-selector-id,.hljs-selector-class,.hljs-quote,.hljs-template-tag,.hljs-deletion{color:#880000}.hljs-title,.hljs-section{color:#880000;font-weight:bold}.hljs-regexp,.hljs-symbol,.hljs-variable,.hljs-template-variable,.hljs-link,.hljs-selector-attr,.hljs-selector-pseudo{color:#BC6060}.hljs-literal{color:#78A960}.hljs-built_in,.hljs-bullet,.hljs-code,.hljs-addition{color:#397300}.hljs-meta{color:#1f7199}.hljs-meta-string{color:#4d99bf}.hljs-emphasis{font-style:italic}.hljs-strong{font-weight:bold}
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/LICENSE:
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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 |
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/README.md:
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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 |
(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. [All GANs Search](https://hollobit.github.io/All-About-the-GAN/search.html)
39 |
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/README.j2.md:
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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 |
(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 |
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/update.py:
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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 |
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/docs/search.html:
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1 |
2 |
3 |
4 | All about the GANs dynamic search
5 |
6 |
7 |
14 |
15 |
16 |
17 |
18 |
19 |
20 |
21 |
22 |
23 |
92 |
93 |
94 |
95 |
127 |
128 |
Copyright (c) 2017-2018, Jonathan Jeon, hollobit@etri.re.kr
140 |
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237 |
238 |
239 |
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/INDEX.j2.md:
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21 | All about the GANs(Generative Adversarial Networks) - Summarized lists for GAN
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What is GANs?
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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.
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(Credit: O’Reilly)
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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)
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What is this list?
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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.
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75 | (Word cloud of Title)
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(Word cloud of Category)
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(Word cloud of Abbr. name)
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This list provides a curated list that merged information from various GAN lists and repositories as below:
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Reference repositories
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[GAN zoo] - A list of all named GANs! by hindupuravinash
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Delving deep into Generative Adversarial Networks (GANs) [Delving] by GKalliatakis
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Awesome GAN for Medical Imaging [Medical] by xinario
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.
(Credit: O’Reilly)
