awesome Generative Adversarial Networks (GANs)

A collection of resources on Generative Adversarial Networks (GANs).

Table of Contents

• Introduction

• 1. Basic Knowledge

• 2. Research Branch

Introduction

The purpose of GAN is to generate/synthesize fake but photo-real images (synthesize high-resolution portraits that are often indistinguishable from real faces). GANs are popular partly because they tackle the important unsolved challenge of unsupervised learning. It represents a zero-sum game between two machine players, a generator and a discriminator, designed to learn the distribution of data. Using an adversarial methods, bypass the need of computing densities, at the expense of a good density estimation.

If intelligence was a cake, unsupervised learning would be the cake, supervised learning would be the icing on the cake, and reinforcement learning would be the cherry on the cake. We know how to make the icing and the cherry, but we don’t know how to make the cake. – Yann LeCun, 2016.

To quickly touch GAN: Stylegan Online Demo

To read some related survey: 2019 2020

SOTA Benchmark: Image Generation

Open Questions about Generative Adversarial Networks: https://distill.pub/2019/gan-open-problems/

Basic Knowledge

• mode collapse: diversity the generator can only learn some limited patterns from the large-scale target datasets, or assigns all of its probability mass to a small region in the space.

• vanishing gradient: the gradient of generator vanishes at the begining of training process.

See more details in the subfolders.

• Classical GANs
• Conditional GAN
• Improving-GANs
• Evaluation Metrics

Research Branch

Branch
Controllable-GAN	Inverion. We can find a common latent code, and we can also find a seperate code for each layer of the generator. It is easy and better to manipulate a given image in the latent feature space.
Few-Shot & Limited Data
Pretrained GANs
Style Transfer & Image2Image
Other Applications	In&Out painting, Text2image, High&Super Reselution, Animation, Video Generation

Loss Function

都是最小化Loss

Name (paper link)	Loss Function
Vanilla GAN	$L_D^{GAN} = - \mathbb{E}_{x \sim p_r} [\log (D(x))] - \mathbb{E}_{\tilde{x} \sim p_g} [\log (1-D(\tilde{x}))] \quad L_G^{GAN} = \mathbb{E}_{\tilde{x} \sim p_g} [\log (1 - D(\tilde{x}))]$
non-saturated GAN	$L_D^{GAN} = - \mathbb{E}_{x \sim p_r} [\log (D(x))] - \mathbb{E}_{\tilde{x} \sim p_g} [\log (1 - D(\tilde{x}))] \quad L_G^{GAN} = -\mathbb{E}_{\tilde{x} \sim p_g} [\log (D(\tilde{x}))] $
LSGAN	$L_D^{LSGAN} = - \mathbb{E}_{x \sim p_r} [(D(x)-1)^2] + \mathbb{E}_{\tilde{x} \sim p_g} [D(\tilde{x})^2] \quad L_G^{LSGAN} = -\mathbb{E}_{\tilde{x} \sim p_g} [(D(\tilde{x})-1)^2]$
WGAN	$L_{D}^{WGAN}= - \mathbb{E}_{x \sim p_r}[D(x)]+\mathbb{E}_{\tilde{x} \sim p_g}[D(\tilde{x})] \quad L_{G}^{WGAN}=-\mathbb{E}_{\tilde{x} \sim p_g}[D(\tilde{x})] \quad W_D \leftarrow \text{clip-by-value}(W_D, -0.01, 0.01) $
WGAN-GP	$L_{D}^{WGAN-GP}=L_{D}^{WGAN} + \lambda {\mathbb{E}}_{\hat{x} \sim p_{\hat{x}}}\left[\left(\left|\nabla_{\hat{x}} D(\hat{x})\right|_{2}-1\right)^{2}\right] \quad L_{G}^{WGAN-GP}=L_{G}^{WGAN} \quad \hat{x} = \alpha x + (1-\alpha) \tilde{x}$
DRAGAN	$L_{D}^{DRAGAN}=L_{D}^{GAN} + \lambda {\mathbb{E}}_{\tilde{x} \sim p_r+\mathcal{N}(0, c)}\left[\left(\left|\nabla_{\hat{x}} D(\tilde{x})\right|_{2}-1\right)^{2}\right] \quad L_{G}^{DRAGAN}=L_{G}^{GAN}$

inspired by hwalsuklee

Some Awesome Codes

studioGAN