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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
Imaginaire is a PyTorch image synthesis library and neural image translation framework designed to generate high-resolution synthetic visual content. It functions as a deep learning visual generator that maps semantic images and videos into photorealistic versions using both supervised and unsupervised methods. The project includes a specialized tool for rendering 3D environments, which converts block-based world representations into photorealistic scenes while maintaining long-term visual consistency. It further supports photorealistic video translation that utilizes reference images to ensu
Keras-GAN is a collection of generative adversarial network implementations built with Keras for synthetic data generation and image manipulation. It provides frameworks for image-to-image translation, image inpainting, and neural image super-resolution. The library includes tools for learning disentangled latent space representations to control specific attributes of synthetic outputs. It also features capabilities for image domain translation using paired or unpaired data and the ability to fill corrupted or missing image parts by analyzing surrounding visual context. The project covers ge
pix2pix is a framework for image-to-image translation using conditional generative adversarial networks. It functions as a supervised trainer and visual domain mapper designed to learn a mapping between input and output images for style and domain transfer. The system utilizes a U-Net encoder-decoder architecture combined with a PatchGAN local discriminator to enforce high-frequency local consistency. It employs L1 loss regularization to ensure generated outputs remain structurally close to the ground truth. The project covers a broad range of computer vision capabilities, including semantic
Generative AI Image and Video Toolset with GANs and Diffusion for Real-World Applications
The main features of jolibrain/joligen are: Computer Vision, Image Translation Frameworks, Transfer Learning Libraries.
Projects with overlapping indexed features include: nvlabs/imaginaire — Imaginaire is a PyTorch image synthesis library and neural image translation framework designed to generate… phillipi/pix2pix — pix2pix is a framework for image-to-image translation using conditional generative adversarial networks. It functions… eriklindernoren/keras-gan — Keras-GAN is a collection of generative adversarial network implementations built with Keras for synthetic data… junyanz/cyclegan — CycleGAN is a generative adversarial network framework designed for unpaired image-to-image translation. It enables… albu/albumentations — Albumentations is an image augmentation library and computer vision preprocessing tool designed to expand datasets for… ailab-cvc/yolo-world — YOLO-World is a vision-language framework and open-vocabulary object detection model. It identifies objects in images…