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.
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.
(Word Cloud of Title)
(Word Cloud of Title)
(Word cloud of Abbr. name)
It provides a list that merged information from various GAN lists and repositories as below:
- [GAN zoo] - A list of all named GANs! by hindupuravinash
- Delving deep into Generative Adversarial Networks (GANs) [Delving] by GKalliatakis
- Awesome GAN for Medical Imaging [Medical] by xinario
- [Adversarial Nets Papers] The classic about Generative Adversarial Networks
- [Really Awesome GAN] by nightrome
- [GANs Paper Collection] by shawnyuen
- [GAN awesome applications] by nashory
- [GAN timeline] by dongb5
- [GAN comparison without cherry-picking] by khanrc
- Collection of generative models in [Keras], [Pytorch version], [Tensorflow version], [Chainer version]
- [Tensor layer]
- [Tensor pack]
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.
Contributions are welcome. Please contact me at [email protected] 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.
{% set count = {'value': 1} %} {% for gan in gans %} {{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'] }})) {%- if count.update({'value': (count.value + 1)}) -%} {% endif %} {%- if gan['Arxiv'] != '-' and gan['Arxiv'] != '' -%} ([arXiv]({{ gan['Arxiv'] }})) {% endif %} {%- if gan['Official_Code'] != '-' and gan['Official_Code'] != '' -%} ([github]({{ gan['Official_Code'] }})) {% endif %} {%- if gan['Tensorflow'] != '-' and gan['Tensorflow'] != '' -%} ([TensorFlow]({{ gan['Tensorflow'] }})) {% endif %} {%- if gan['PyTorch'] != '-' and gan['PyTorch'] != '' -%} ([PyTorch]({{ gan['PyTorch'] }})) {% endif %} {%- if gan['KERAS'] != '-' and gan['KERAS'] != '' -%} ([KERAS]({{ gan['KERAS'] }})) {% endif %} {%- if gan['Web'] != '-' and gan['Web'] != '' -%} ([Web]({{ gan['Web'] }})) {% endif %}
- {%- if gan['Citations'] | int > 50 %} :dart: {% endif %}{%- if gan['Stars'] | int > 10 %} 
{% endif %} {{ gan['Year'] }}/{{ gan['Month'] }} {# #}
{%- if gan['Medical'] != '-' -%} Medical: {{ gan['Medical'] }} {% endif %}
{%- if gan['Category'] != '-' -%} {{ gan['Category'] }} {% endif %}
{%- if gan['Abbr.'] != '-' and gan['Abbr.'] != '' %} {{ gan['Abbr.'] }} {% endif %}
{%- if gan['Citations'] != '0' and gan['Citations'] != '' %} Citation: {{ gan['Citations'] }} {% endif %}
{%- if gan['Stars'] != '-' and gan['Stars'] != '' %} Stars: {{ gan['Stars'] }} {% endif %}
{% endfor %}
MIT (c) 2017, 2018 Jonathan Jeon