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The author's officially unofficial PyTorch BigGAN implementation.
This project is a deep learning research toolkit and generative model library providing implementations of Variational Autoencoders using the PyTorch framework. It serves as a framework for training and evaluating autoencoder architectures to learn latent representations for data reconstruction and the generation of synthetic data samples. The toolkit focuses on unsupervised feature learning and generative model training, featuring a system for mapping external configuration files to model hyperparameters to ensure reproducible experimental runs. It includes mechanisms for tracking training p
A pytorch implementation of Paper "Improved Training of Wasserstein GANs"
A clean and readable Pytorch implementation of CycleGAN
The main features of unit8co/vegans are: Probabilistic and Generative, GANs, VAEs, and AEs.
Projects with overlapping indexed features include: ajbrock/biggan-pytorch — The author's officially unofficial PyTorch BigGAN implementation. antixk/pytorch-vae — This project is a deep learning research toolkit and generative model library providing implementations of Variational… caogang/wgan-gp — A pytorch implementation of Paper "Improved Training of Wasserstein GANs". catalyst-team/gan — Catalyst.GAN. csailvision/gandissect — Pytorch-based tools for visualizing and understanding the neurons of a GAN. https://gandissect.csail.mit.edu/. aitorzip/pytorch-cyclegan — A clean and readable Pytorch implementation of CycleGAN.