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Study Generative Adversarial Networks

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

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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

Based on: https://github.com/hwalsuklee/tensorflow-generative-model-collections

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Study Generative Adversarial Networks

tensorflow pytorch gan dcgan pix2pix cgan dualgan cdcgan

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