Generative adversarial network: Difference between revisions
| Line 16: | Line 16: | ||
===InfoGAN=== | ===InfoGAN=== | ||
===SinGAN=== | ===SinGAN=== | ||
{{ main | | {{ main | SinGAN}} | ||
[https://arxiv.org/abs/1905.01164 Paper]<br> | [https://arxiv.org/abs/1905.01164 Paper]<br> | ||
[http://webee.technion.ac.il/people/tomermic/SinGAN/SinGAN.htm Website]<br> | [http://webee.technion.ac.il/people/tomermic/SinGAN/SinGAN.htm Website]<br> | ||
[https://github.com/tamarott/SinGAN Github Official PyTorch Implementation]<br> | [https://github.com/tamarott/SinGAN Github Official PyTorch Implementation]<br> | ||
SinGAN: Learning a Generative Model from a Single Natural Image<br> | SinGAN: Learning a Generative Model from a Single Natural Image<br> | ||
Revision as of 18:49, 5 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 Generator
Update Discriminator
Variations
CycleGan
InfoGAN
SinGAN
Paper
Website
Github Official PyTorch Implementation
SinGAN: Learning a Generative Model from a Single Natural Image