# pytorch-spectral-normalization-gan

**Repository Path**: zyabo/pytorch-spectral-normalization-gan

## Basic Information

- **Project Name**: pytorch-spectral-normalization-gan
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-12-17
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# SN-GAN (spectral normalization GAN) in PyTorch
Based on the paper "Spectral Normalization for Generative Adversarial Networks" by Takeru Miyato, Toshiki Kataoka, Masanori Koyama, Yuichi Yoshida

ICLR 2018 preprint:
https://openreview.net/forum?id=B1QRgziT-

## CIFAR-10 Samples
![with spectral normalization](https://github.com/christiancosgrove/pytorch-spectral-normalization-gan/blob/master/with_sn.png?raw=true)

## Implementation Details
This code implements both DCGAN-like and ResNet GAN architectures. In addition, training with standard, Wasserstein, and hinge losses is possible. 

To get ResNet working, initialization (Xavier/Glorot) turned out to be very important.

## Training
Train ResNet generator and discriminator with hinge loss: `python main.py --model resnet --loss hinge`

Train ResNet generator and discriminator with wasserstein loss: `python main.py --model resnet --loss wasserstein`

Train DCGAN generator and discriminator with cross-entropy loss: `python main.py --model dcgan --loss bce`