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Generative Adversarial Text-to-Image Synthesis
The main features of reedscot/icml2016 are: Generative Adversarial Networks, Image Synthesis, Multimodal Learning.
Open-source alternatives to reedscot/icml2016 include: hanzhanggit/stackgan — Pytorch implementation. phillipi/pix2pix — pix2pix is a framework for image-to-image translation using conditional generative adversarial networks. It functions… lynnho/attgan-tensorflow — [TIP'19] AttGAN: Facial Attribute Editing by Only Changing What You Want. goodfeli/adversarial — This project is a generative adversarial network implementation and research framework. It provides the tools and… junyanz/igan — iGAN is a framework for producing synthetic images using generative adversarial networks. It provides a web-based… kozistr/awesome-gans — Awesome-GANs is a curated resource list and research repository focused on the development and evaluation of…
iGAN is a framework for producing synthetic images using generative adversarial networks. It provides a web-based interface for interactively creating and editing imagery across categories such as landscapes, architecture, and fashion using pre-trained models. The system enables precise control over visual output through latent space exploration, interpolation, and projection. Users can guide the generative process using an interactive editor featuring sketching, coloring, and warping brushes to refine specific regions or shapes in real-time. The project supports both automated scripted gene
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
TIP'19 AttGAN: Facial Attribute Editing by Only Changing What You Want
