Awesome
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
<img src="img/cyclegan_58_11603.png" height=300 /> Result after 3 hours and 58 epochs on a GTX 1080. From top to bottom: Input, Fake, Recreate of the input. <img src="img/cyclegan_face.png" height=300 /> 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
<img src="img/pix2pix.png" height="200" /> Validation result of edges-to-shoes after 12 epochs. From top to bottom: Input, Ground truth, the result. <img src="img/pix2pix_resnet.png" height="200" /> 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
<img src="img/infogan-mnist.png" height="300" />DCGAN
- dcgan-lasagne: DCGAN in Lasagne