How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.
StarGAN v2 - Official PyTorch Implementation (CVPR 2020)
CycleGAN is a generative adversarial network framework designed for unpaired image-to-image translation. It enables the conversion of images between two distinct visual domains using datasets that do not require direct one-to-one matching examples. The project implements a deep learning style transfer tool capable of artistic style transfer, object transfiguration, and domain-to-domain conversion. It uses a dual-generator architecture and cycle-consistency loss to ensure that images translated to a target domain and back recover their original state. The framework covers core machine learnin
PyTorch implementation of "Learning to Discover Cross-Domain Relations with Generative Adversarial Networks"
Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks
The main features of chuanli11/mgans are: Domain Transfer and Translation.
Projects with overlapping indexed features include: carpedm20/discogan-pytorch — PyTorch implementation of "Learning to Discover Cross-Domain Relations with Generative Adversarial Networks". clovaai/stargan-v2 — StarGAN v2 - Official PyTorch Implementation (CVPR 2020). fxia22/pixeldtgan — A torch implementation of "Pixel-Level Domain Transfer". junyanz/cyclegan — CycleGAN is a generative adversarial network framework designed for unpaired image-to-image translation. It enables… luanfujun/deep-photo-styletransfer — This project is a deep learning style transfer framework designed to apply artistic styles to photographs. It… nvlabs/munit — Multimodal Unsupervised Image-to-Image Translation.
