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
This project is a deep learning framework designed for training and deploying image-to-image translation models. It serves as a research platform for experimenting with neural network architectures that transform visual content between distinct stylistic domains, supporting both paired and unpaired training data. The framework distinguishes itself through its support for cycle-consistency constraints, which allow for image translation between domains without requiring corresponding paired examples. It provides a structured pipeline that utilizes adversarial loss optimization, where generator
PyTorch implementation of "Learning to Discover Cross-Domain Relations with Generative Adversarial Networks"
This project is an unsupervised image restoration tool that uses a convolutional neural network as a structural prior to reconstruct images from noisy or incomplete data. It functions as a neural network image prior, utilizing the inherent biases of the network architecture to restore pixels without the need for a pre-trained dataset or external learning. The system performs zero-shot image restoration by treating the network architecture itself as a regularization term. It uses a randomly initialized encoder-decoder structure and iterative gradient descent to minimize pixel-wise loss, recove
We have a reimplementation of the UNIT method that is more performant. It is avaiable at Imaginaire
Les fonctionnalités principales de mingyuliutw/unit sont : Computer Vision Models, Generative Models, Image Translation, Model Implementations.
Les alternatives open-source à mingyuliutw/unit incluent : nvidia/pix2pixhd — pix2pixHD is a conditional generative adversarial network designed to transform semantic label maps into… mrzhu-cool/pix2pix-pytorch — PyTorch implementation of Image-to-Image Translation Using Conditional Adversarial Networks. carpedm20/discogan-pytorch — PyTorch implementation of "Learning to Discover Cross-Domain Relations with Generative Adversarial Networks". dmitryulyanov/deep-image-prior — This project is an unsupervised image restoration tool that uses a convolutional neural network as a structural prior… kentsyx/neural-image-assessment — This is a PyTorch implementation of the paper NIMA: Neural IMage Assessment (accepted at IEEE Transactions on Image… junyanz/pytorch-cyclegan-and-pix2pix — This project is a deep learning framework designed for training and deploying image-to-image translation models. It…