Generative adversarial network: Difference between revisions

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For iteration i
For iteration i
   For iteration j
   For iteration j
     Update Generator
     Update Discriminator
   Update Discriminator
   Update Generator
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==Variations==
==Variations==
===Wasserstein GAN===
[https://arxiv.org/pdf/1704.00028.pdf Paper]<br>
[https://medium.com/@jonathan_hui/gan-wasserstein-gan-wgan-gp-6a1a2aa1b490 Medium post]<br>
This new WGAN-GP loss function improves the stability of training.
==Applications==
===CycleGan===
===CycleGan===
===InfoGAN===
===InfoGAN===

Revision as of 13:19, 12 November 2019

GANs are generative adversarial networks. They were developed by Ian Goodfellow.
Goal: Learn to generate examples from the same distribution as your training set.

Basis Structure

GANs consist of a generator and a discriminator.

For iteration i
  For iteration j
    Update Discriminator
  Update Generator

Variations

Wasserstein GAN

Paper
Medium post
This new WGAN-GP loss function improves the stability of training.

Applications

CycleGan

InfoGAN

SinGAN

Paper
Website
Github Official PyTorch Implementation
SinGAN: Learning a Generative Model from a Single Natural Image