wgan, wgan2(improved, gp), infogan, and dcgan implementation in lasagne, keras, pytorch.

Generative Adversarial Notebooks

Collection of my Generative Adversarial Network implementations

Most codes are for python3, most notebooks works on

CycleGAN

CycleGAN-lasagne
CycleGAN-keras

CycleGAN results

Result after 3 hours and 58 epochs on a GTX 1080. From top to bottom: Input, Fake, Recreate of the input.

Face-off result. From top to bottom: Input, Fake, Recreate of the input. youtube video

pix2pix

pix2pix-keras: pix2pix GAN Keras implementation
pix2pix-lasagne: pix2pix GAN Lasagne implementation
pix2pix-torch: pix2pix GAN pytorch implementation

pix2pix sample results

Validation result of edges-to-shoes after 12 epochs. From top to bottom: Input, Ground truth, the result. Validation result of facades dataset after 150 epochs using resnet. From top to bottom: Input, Ground truth, the result.

WGAN on CIFAR10

wgan-keras: Wasserstein GAN Keras implementation
wgan-lasagne: Wasserstein GAN Lasagne implementation
wgan-torch: Wasserstein GAN pytorch implementation based on https://github.com/martinarjovsky/WassersteinGAN

WGAN2 (improved WGAN/WGAN-gp)

wgan2-lasagne: improved WGAN Lasagne implementation (on CIFAR10)
wgan2-keras: improved WGAN Keras implementation (on CIFAR10)
wgan2-lasagne-anime: WGAN on anime face images, lasagne
wgan2-AC-lasagne: improved WGAN Lasagne implementation with Auxillary classfier

WGAN2 sample results

cifar10 dataset

cifar10 dataset with Auxillary classfier

anime face dataset

InfoGAN

mnist-infogan: InfoGAN Lasagne on MNIST dataset
mnist-infogan-paper-uniform: InfoGAN Lasagne on MNIST dataset (fllowing the paper implementation)

InfoGAN sample results

DCGAN

dcgan-lasagne: DCGAN in Lasagne

GANotebooks