1703.10593

N/A

Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks Image-to-image translation is a class of vision and graphics problems where the goal is to learn the mapping between an input image and an output image using a training set of aligned image pairs. However, for many tasks, paired training data will not be available. We present an approach for learning to translate an image from a source domain $X$ to a target domain $Y$ in the absence of paired examples. Our goal is to learn a mapping $G: X \rightarrow Y$ such that the distribution of images from $G(X)$ is indistin

saved by

• Mike Liang

related reading

• [2111.05826] Palette: Image-to-Image Diffusion Modelsarxiv.org
• Yang Songyang-song.net
• Feature-wise transformationsdistill.pub
• ⭐️ Diffusion Modelsandrewkchan.dev
• GitHub - ml5js/training-styletransfer: Style Transfer training and using the model in ml5js · GitHubgithub.com
• A Tale of Two Features: Stable Diffusion Complements DINO for Zero-Shot Semantic Correspondencesd-complements-dino.github.io
• Annotated Research Paper Implementations: Transformers, StyleGAN, Stable Diffusion, DDPM/DDIM, LayerNorm, Nucleus Sampling and morenn.labml.ai
• An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversiontextual-inversion.github.io
• Extending Stein's unbiased risk estimator to train deep denoisers with correlated pairs of noisy imagesproceedings.neurips.cc
• Bare-bones Diffusion Modelsmadebyoll.in
• Unsupervised Learning with SURE.pdfarxiv.org
• [1603.08155] Perceptual Losses for Real-Time Style Transfer and Super-Resolutionarxiv.org