MingtaoGuo/DCGAN_WGAN_WGAN-GP_LSGAN_SNGAN_RSGAN_BEGAN_ACGAN_PGGAN_TensorFlow MingtaoGuo/DCGAN_WGAN_WGAN-GP_LSGAN_SNGAN_RSGAN_BEGAN_ACGAN_PGGAN_TensorFlow

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  • Created over 6 years ago
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MingtaoGuo/DCGAN_WGAN_WGAN-GP_LSGAN_SNGAN_RSGAN_BEGAN_ACGAN_PGGAN_TensorFlow
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MingtaoGuo

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Repository Details

Implementation of some different variants of GANs by tensorflow, Train the GAN in Google Cloud Colab, DCGAN, WGAN, WGAN-GP, LSGAN, SNGAN, RSGAN, RaSGAN, BEGAN, ACGAN, PGGAN, pix2pix, BigGAN

DCGAN_LSGAN_WGAN_WGAN-GP_SNGAN_RSGAN_BEGAN_ACGAN_PGGAN_pix2pix_BigGAN

Implementation of some different variants of GANs

Introduction

This code is mainly implement some basic GANs about 'DCGAN', 'WGAN', 'WGAN-GP', 'LSGAN', 'SNGAN', 'RSGAN'&'RaSGAN', 'BEGAN', 'ACGAN', 'PGGAN', 'pix2pix', 'BigGAN'.

More details of these GANs, please see follow papers:

  1. DCGAN: Unsupervised representation learning with deep convolutional generative adversarial networks

  2. WGAN: Wasserstein gan

  3. WGAN-GP: Improved training of wasserstein gans

  4. LSGAN: Least Squares Generative Adversarial Networks

  5. SNGAN: Spectral normalization for generative adversarial networks

  6. RSGAN&RaSGAN: The relativistic discriminator: a key element missing from standard GAN

  7. BEGAN:BEGAN: Boundary Equilibrium Generative Adversarial Networks

  8. ACGAN: Conditional Image Synthesis With Auxiliary Classifier GANs

  9. PGGAN: Progressive Growing of GANs for Improved Quality, Stability, and Variation

  10. pix2pix: Image-to-Image Translation with Conditional Adversarial Networks

  11. BigGAN: Large Scale GAN Training for High Fidelity Natural Image Synthesis [Code]

Attention

If your computer don't have GPU to accelerate the training process, please click Google Cloud Colab to train the GANs.

How to use

Firstly, you should download the data 'facedata.mat' from Baidu Drive or Google Drive, then put the file 'facedata.mat' into the folder 'TrainingSet'.

Requirements

  1. python3.5
  2. tensorflow1.4.0
  3. pillow
  4. scipy
  5. numpy

Results of this code

This result is using DCGAN trained about 8000 iterations.

Compare LSGAN, WGAN, WGAN-GP, SNGAN, RSGAN of different iteration

Convergence of BEGAN

ACGAN for face generating

dataset: download address: Baidu Drive password: 5egd

Fixed label, change noise slightly Fixed noise, change label slightly

PGGAN for face generating

SNGAN for cifar-10

D_loss G_loss results

Pix2Pix

Dataset: Google maps download address: http://efrosgans.eecs.berkeley.edu/pix2pix/datasets/maps.tar.gz

Edges2Shoes download address: http://efrosgans.eecs.berkeley.edu/pix2pix/datasets/edges2shoes.tar.gz

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