How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.
A list of all named GANs!
This project is a generative adversarial network implementation and research framework. It provides the tools and hyperparameters necessary to train and evaluate generative models across various datasets, specifically designed to reproduce results from academic research. The framework includes a Parzen density likelihood estimator to calculate model log likelihood. This allows for the quantitative evaluation of generative distributions and the measurement of overall model performance. The codebase covers machine learning research capabilities, focusing on the training of adversarial networks
The main features of evolving-ai-lab/ppgn are: Generative Adversarial Networks, Generative Models.
Open-source alternatives to evolving-ai-lab/ppgn include: hindupuravinash/the-gan-zoo — A list of all named GANs! nvlabs/stylegan — StyleGAN is a TensorFlow-based generative adversarial network framework designed for the synthesis of high-resolution… goodfeli/adversarial — This project is a generative adversarial network implementation and research framework. It provides the tools and… hanzhanggit/stackgan — Pytorch implementation. martinarjovsky/wassersteingan — Wasserstein GAN. nvlabs/stylegan2 — StyleGAN2 is a TensorFlow generative adversarial network and image synthesis model designed to produce high-resolution…
