Continuous Conditional Generative Adversarial Networks (CcGAN)

使用Pytorch实现GAN 的过程

Keras implementations of Generative Adversarial Networks.

QuantImPy, a Python library for scientific image processing.

Implementation of a Wasserstein Generative Adversarial Network with Gradient Penalty to enforce lipchitz constraint. The WGAN utilizes the wasserstein loss or critic as its loss function instead of…

For beginner, this will be the best start for VAEs, GANs, and CVAE-GAN. This contains AE, DAE, VAE, GAN, CGAN, DCGAN, WGAN, WGAN-GP, VAE-GAN, CVAE-GAN. All use PyTorch.

Pytorch implementation of a Conditional WGAN with Gradient Penalty