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PyTorch implementation of "Learning to Discover Cross-Domain Relations with Generative Adversarial Networks"
The main features of carpedm20/discogan-pytorch are: Domain Transfer and Translation, Generative Models, Image Translation, Model Implementations.
Projects with overlapping indexed features include: nvidia/pix2pixhd — pix2pixHD is a conditional generative adversarial network designed to transform semantic label maps into… phillipi/pix2pix — pix2pix is a framework for image-to-image translation using conditional generative adversarial networks. It functions… junyanz/cyclegan — CycleGAN is a generative adversarial network framework designed for unpaired image-to-image translation. It enables… mingyuliutw/unit — We have a reimplementation of the UNIT method that is more performant. It is avaiable at Imaginaire. nvlabs/munit — Multimodal Unsupervised Image-to-Image Translation. yunjey/stargan — StarGAN is a PyTorch image-to-image translation framework designed to synthesize visual styles and attributes across…
We have a reimplementation of the UNIT method that is more performant. It is avaiable at Imaginaire
pix2pixHD is a conditional generative adversarial network designed to transform semantic label maps into high-resolution photorealistic images. It functions as a high-resolution image synthesizer and an image-to-image translation model capable of producing synthetic images at 2048x1024 resolution. The system includes a semantic image editor that allows for the modification of high-resolution visuals by updating the underlying semantic label maps. This enables interactive image editing and the generation of photorealistic images based on source images or discrete label maps. The framework pro
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
