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

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](https://www.youtube.com/watch?v=Fea4kZq0oFQ)

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

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

DCGAN sample results