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Wasserstein GAN
The main features of martinarjovsky/wassersteingan are: Generative Adversarial Networks, Generative Models, Model Implementations, Security and Privacy, Theory and Training, GANs, VAEs, and AEs.
Projects with overlapping indexed features include: junyanz/pytorch-cyclegan-and-pix2pix — This project is a deep learning framework designed for training and deploying image-to-image translation models. It… nvidia/pix2pixhd — pix2pixHD is a conditional generative adversarial network designed to transform semantic label maps into… caogang/wgan-gp — A pytorch implementation of Paper "Improved Training of Wasserstein GANs". dmitryulyanov/age — Code for the paper "Adversarial Generator-Encoder Networks". mailmahee/pytorch-generative-adversarial-networks — simple generative adversarial network (GAN) using PyTorch. openai/improved-gan — Code for the paper "Improved Techniques for Training GANs".
This project is a deep learning framework designed for training and deploying image-to-image translation models. It serves as a research platform for experimenting with neural network architectures that transform visual content between distinct stylistic domains, supporting both paired and unpaired training data. The framework distinguishes itself through its support for cycle-consistency constraints, which allow for image translation between domains without requiring corresponding paired examples. It provides a structured pipeline that utilizes adversarial loss optimization, where generator
A pytorch implementation of Paper "Improved Training of Wasserstein GANs"
simple generative adversarial network (GAN) using PyTorch
