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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
A repository for the paper "Improved Adversarial Systems for 3D Object Generation and Reconstruction".
Code for the paper "Improved Techniques for Training GANs"
The main features of openai/improved-gan are: Generative Adversarial Networks, Generative Modeling Foundations, Generative Models, Security and Privacy.
Projects with overlapping indexed features include: martinarjovsky/wassersteingan — Wasserstein GAN. goodfeli/adversarial — This project is a generative adversarial network implementation and research framework. It provides the tools and… hindupuravinash/the-gan-zoo — A list of all named GANs! evolving-ai-lab/ppgn. hanzhanggit/stackgan — Pytorch implementation. edwardsmith1884/3d-iwgan — A repository for the paper "Improved Adversarial Systems for 3D Object Generation and Reconstruction".
