Awesome Generative Adversarial Networks

A curated list of awesome Generative Adversarial Networks papers, experiments, libraries, and projects.

What is GANs?

Generative adversarial networks are a branch of unsupervised machine learning, implemented by a system of two neural networks competing against each other in a zero-sum game framework.

There are many interesting recent development in deep learning... The most important one, in my opinion, is adversarial training (also called GAN for Generative Adversarial Networks). This, and the variations that are now being proposed is the most interesting idea in the last 10 years in ML, in my opinion.

– Yann LeCun, The prominent deep learning researcher and director of AI research at Facebook

papers

• Generative Adversarial Networks (the first paper)

codes

• goodfeli/adversarial: (the first code)
• Newmu/dcgan_code: Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
• carpedm20/DCGAN-tensorflow: A tensorflow implementation of "Deep Convolutional Generative Adversarial Networks"
• soumith/dcgan.torch: A torch implementation of http://arxiv.org/abs/1511.06434: Train your own image generator
• jacobgil/keras-dcgan: Keras implementation of Deep Convolutional Generative Adversarial Networks
• facebook/eyescream: natural image generation using ConvNets
• musyoku/adversarial-autoencoder: Chainer implementation of adversarial autoencoder (AAE)
• paarthneekhara/text-to-image: Text to image synthesis using thought vectors
• dpkingma/nips14-ssl:
• openai/improved-gan:
• yujiali/gmmn:
• dyelax/Adversarial_Video_Generation:
• phillipi/pix2pix: Image-to-image translation using conditional adversarial nets https://phillipi.github.io/pix2pix/
• openai/cleverhans: A library for benchmarking vulnerability to adversarial examples
• junyanz/iGAN: Interactive Image Generation via Generative Adversarial Networks
• devnag/pytorch-generative-adversarial-networks: A very simple generative adversarial network (GAN) in PyTorch

videos

• Generative Adversarial Networks, by Ian Goodfellow
• Tutorial on Generative Adversarial Networks by Mark Chang