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Study Generative Adversarial Networks

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results
 
 
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README.md
 
 
acgan.py
 
 
cgan.py
 
 
dataset_loader.py
 
 
gan.py
 
 
gansgp.py
 
 
losses.py
 
 
main.py
 
 
network.py
 
 
params.json
 
 
projdgan.py
 
 
utils.py
 
 
wgan.py
 
 
wgangp.py
 
 

README.md

GAN

  • Generative Adversarial Networks
    • GAN
    • CGAN
    • WGAN
    • WGAN-GP

Results - MNIST

Type Generation Loss
GAN
WGAN
WGAN-GP
CGAN
ACGAN

Results - fashion-MNIST

Type Generation Loss
GAN
WGAN
WGAN-GP
CGAN
ACGAN

Prerequisites

  • Ubuntu 16.04
  • tensorflow-gpu==1.13.1
  • tensorflow-datasets==1.0.1

Data sets

  • MNIST & fashion-MNIST: dataset_loader.py

Reference

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Study Generative Adversarial Networks

Topics

gan tensorflow pytorch dcgan cgan cdcgan pix2pix dualgan

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