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Collection of generative models in Tensorflow
Resources and Implementations of Generative Adversarial Nets: GAN, DCGAN, WGAN, CGAN, InfoGAN
Collection of generative models in Pytorch version.
learn code with tensorflow
Annotated, understandable, and visually interpretable PyTorch implementations of: VAE, BIRVAE, NSGAN, MMGAN, WGAN, WGANGP, LSGAN, DRAGAN, BEGAN, RaGAN, InfoGAN, fGAN, FisherGAN
Simple Implementation of many GAN models with PyTorch.
PyTorch Implementation of InfoGAN
Vanilla GAN implemented on top of keras/tensorflow enabling rapid experimentation & research. Branches correspond to implementations of stable GAN variations (i.e. ACGan, InfoGAN) and other promising variations of GANs like conditional and Wasserstein.
implement infoGAN using pytorch
🎎 InfoGAN: Interpretable Representation Learning
InfoGAN inspired neural network trained on zap50k images (using Tensorflow + tf-slim). Intermediate layers of the discriminator network are used to do image similarity.
Pytorch implementations of generative models: VQVAE2, AIR, DRAW, InfoGAN, DCGAN, SSVAE
Repository for implementation of generative models with Tensorflow 1.x
Generative models (GAN, VAE, Diffusion Models, Autoregressive Models) implemented with Pytorch, Pytorch_lightning and hydra.
ProGAN with Standard, WGAN, WGAN-GP, LSGAN, BEGAN, DRAGAN, Conditional GAN, InfoGAN, and Auxiliary Classifier GAN training methods
Simple implementation of Least Squares Generative Adversarial Networks
Generative Adversarial Networks with TensorFlow2, Keras and Python (Jupyter Notebooks Implementations)
Performance comparison of ACGAN, BEGAN, CGAN, DRAGAN, EBGAN, GAN, infoGAN, LSGAN, VAE, WGAN, WGAN_GP on cifar-10