Generative Adversarial Networks (GAN) is a framework for estimating generative models via an adversarial process by training two models simultaneously. A generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. It was proposed and presented in Advances in Neural Information Processing Systems (NIPS) 2014.

Introduction

  • Taxonomy of Machine Learning ML
    • Supervised learning: The discriminative model learns how to classify input to its class. Discriminative
    • Unsupervised learning: The generative model learns the distribution of training data. Generative
      • More challenging than supervised learning because there is no label or curriculum which leads self learning
      • NN solutions: Boltzmann machine, Auto-encoder, Variation Inference, GAN
  • The goal of the generative model is to find a that approximates well. Goal

Generative Adversarial Nets (GAN)

Intuition of GAN

  • The discriminator D should classify a real image as real ( close to 1) and a fake image as fake ( close to 0).
  • The generator G should create an image that is indistinguishable from real to deceive the discriminator ( close to 1). Intuition

Explanation

Objective Function

  • Objective function of GAN is minimax game of two-player G and D.
  • For the discriminator D should maximize
    • Sample from real data distribution for
    • Sample latent code from Gaussian distribution for
    • is maximum when and
  • For the generator G should minimize
    • G is independent of
    • is minimum when
  • Saturating problem
    • In practice, early in learning, when G is poor, D can reject samples with high confidence because they are clearly different from the training data.
    • In this case, the gradient is relatively small at which makes saturates. Saturate
    • Rather than training G to minimize , we can train G to maximize .
    • This objective function results in the same fixed point of the dynamics of G and D but provides much stronger gradients early in learning. Non-Saturate
  • Why does GANs work?
    • Because it actually minimizes the distance between the real data distribution and the model distribution .
      • Jensen-Shannon divergence (JSD) is a method of measuring the similarity between two probability distributions based on the Kullback-Leibler divergence (KL).

Why

Variants of GAN

Deep Convolutional GAN (DCGAN), 2015

  • DCGAN used convolution for discriminator and deconvolution for generator.
    • It is stable to train in most settings compared to GANs.
    • DCGAN used the trained discriminators for image classification tasks, showing competitive performance with other unsupervised algorithms.
    • Specific filters of DCGAN have learned to draw specific objects.
    • The generators have interesting vector arithmetic properties allowing for easy manipulation of many semantic qualities of generated samples.

DCGAN

  • Latent vector arithmetic
    • They showed consistent and stable generations that semantically obeyed the linear arithmetic including object manipulation and face pose.

Arithmetic

Least Squares GAN (LSGAN), 2016

  • LSGAN adopt the least squares loss function for the discriminator instead of cross entropy loss function of GAN.
    • Since, cross entropy loss function may lead to the vanishing gradient problem.
    • LSGANs are able to generate higher quality images than regular GANs.
    • LSGANs performs more stable during the learning process.

LSGAN

Semi-Supervised GAN (SGAN), 2016

  • SGAN extend GANs that allows them to learn a generative model and a classifier simultaneously.
    • SGAN improves classification performance on restricted data sets over a baseline classifier with no generative component.
    • SGAN can significantly improve the quality of the generated samples and reduce training times for the generator.

SGAN

Auxiliary Classifier GAN (ACGAN), 2016

  • ACGAN is added more structure to the GAN latent space along with a specialized cost function results in higher quality samples.

ACGAN

Extensions of GAN

CycleGAN: Unpaired Image-to-Image Translation

  • CycleGAN presents a GAN model that transfer an image from a source domain A to a target domain B in the absence of paired examples.
    • The generator should generates a horse from the zebra to deceive the discriminator .
    • generates a reconstructed image of domain A which makes the shape to be maintained when generates a horse image from the zebra.

CycleGAN

  • Result CycleGAN Result

StackGAN: Text to Photo-realistic Image Synthesis

  • StackGAN generate photo-realistic images conditioned on text descriptions.

StackGAN

  • Result StackGAN Result

Latest work

  • Visual Attribute Transfer
    • Jing Liao et al. Visual Attribute Transfer through Deep Image Analogy, 2017

Visual

  • User-Interactive Image Colorization
    • Richard Zhang et al. Real-Time User-Guided Image Colorization with Learned Deep Prioirs, 2017

Colorization

References

  • Paper: Generative Adversarial Nets [Link]
  • Paper: Unsupervised representation learning with deep convolutional generative adversarial networks [Link]
  • Paper: Least Squares Generative Adversarial Networks [Link]
  • Paper: Semi-Supervised Learning with Generative Adversarial Networks [Link]
  • Paper: Conditional Image Synthesis With Auxiliary Classifier GANs [Link]
  • Paper: Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks [Link]
  • Paper: StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks [Link]
  • PPT: Generative Adversarial Nets by Yunjey Choi [Link]

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