Pytorch implementation of DCGAN, CDCGAN, LSGAN, WGAN and WGAN-GP for CelebA dataset.

$ git clone https://github.com/AndrewZhuZJU/Pytorch_GAN_CelebA.git
$ cd Pytorch_GAN_CelebA

To train any GAN please use main_**.py. For example,

$ python main_DCGAN.py

Not that all the setting parameters for the modesl are in args.py, please change properly.

DCGAN	LSGAN	CGAN(Up:Male, Bottom:Female)
WGAN	WGAN-GP

DCGAN	LSGAN	CGAN(Up:Male, Bottom:Female)
WGAN	WGAN-GP

• Inception score calculated in Epoch 40 (30K generated images)

• Inception score calculated in Epoch 20 (30K generated images)

Take DCGAN for example:

Generator Loss

Discriminator Loss

• Ubuntu 16.04 LTS
• NVIDIA GTX 1080 Ti
• CUDA 9.0
• pytorch 0.4
• python 2.7
• Others Dependencies: numpy, imageio, torchvision, tensorboard, etc.

1.Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

2.Conditional Generative Adversarial Nets

3.Wasserstein GAN

4.Improved Training of Wasserstein GANs

5.Least Squares Generative Adversarial Networks