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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 simple interface for editing natural photos with generative neural networks.
[TIP'19] AttGAN: Facial Attribute Editing by Only Changing What You Want
The main features of lynnho/attgan-tensorflow are: Facial Manipulation, Generative Adversarial Networks, Image Synthesis.
Projects with overlapping indexed features include: junyanz/igan — iGAN is a framework for producing synthetic images using generative adversarial networks. It provides a web-based… hanzhanggit/stackgan — Pytorch implementation. ajbrock/neural-photo-editor — A simple interface for editing natural photos with generative neural networks. guim3/icgan — Invertible conditional GANs for image editing. goodfeli/adversarial — This project is a generative adversarial network implementation and research framework. It provides the tools and… phillipi/pix2pix — pix2pix is a framework for image-to-image translation using conditional generative adversarial networks. It functions…
