README.md

GANs

Simple Pytorch implementations of most used Generative Adversarial Network (GAN) varieties.

GPU or CPU

Support both GPU and CPU.

Dependencies

• Anaconda (Python 2.7)
• PyTorch 0.4.0

Table of Contents

• Vanilla GAN (GAN)
• Conditonal GAN (cGAN)
• Improved Conditonal GAN (Improved cGAN)
• Deep Convolutional GAN (DCGAN)
• Wasserstein GAN (WGAN)
• Improved Training of Wasserstein GAN (WGAN-GP)

Experiment Results

Vanilla GAN (GAN)

epoch 0	epoch 10	epoch 20	epoch 30	epoch 40
epoch 50	epoch 100	epoch 150	epoch 199	-
				-

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Conditional GAN (cGAN)

epoch 0	epoch 10	epoch 20	epoch 30	epoch 40
epoch 50	epoch 100	epoch 150	epoch 199	-
				-

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Improved Conditional GAN (Improved cGAN)

epoch 0	epoch 10	epoch 20	epoch 30	epoch 40
epoch 50	epoch 100	epoch 150	epoch 199	-
				-

Deep Convolutional GAN (DCGAN)

epoch 0	epoch 10	epoch 20	epoch 30	epoch 40
epoch 50	epoch 60	epoch 70	epoch 80	epoch 90

Wasserstein GAN (WGAN)

epoch 0	epoch 10	epoch 20	epoch 30	epoch 40
epoch 50	epoch 100	epoch 150	epoch 199	-
				-

Wasserstein GAN with Gradient Plenty (WGAN-GP)

epoch 0	epoch 10	epoch 20	epoch 30	epoch 40
epoch 50	epoch 100	epoch 150	epoch 199	-
				-

Acknowledgement

This project is going with the GAN Theory and Practice part of the Deep Learning Course: from Algorithm to Practice.

Contacts

If you have any question about the project, please feel free to contact with me.

E-mail: guan.wang0706@gmail.com