NeuralOptimalTransport and KernelNeuralOptimalTransport
The tools are ecosystem siblings, where B is a specific version or extension of A, building upon the core concepts of "Neural Optimal Transport" by incorporating kernel methods as detailed in the "Kernel Neural Optimal Transport" paper, likely by the same author.
About NeuralOptimalTransport
iamalexkorotin/NeuralOptimalTransport
PyTorch implementation of "Neural Optimal Transport" (ICLR 2023 Spotlight)
This project helps researchers and machine learning practitioners transform images from one style or domain to another without needing paired examples. You input two collections of images (e.g., photos of shoes and photos of handbags), and it outputs new images that visually translate items from the first collection into the style of the second. This is ideal for those working on generative AI and computer vision tasks.
About KernelNeuralOptimalTransport
iamalexkorotin/KernelNeuralOptimalTransport
PyTorch implementation of "Kernel Neural Optimal Transport" (ICLR 2023)
This project helps researchers and machine learning practitioners perform image-to-image translation between different visual domains, even when there are no paired examples. It takes images from one category (like female faces) and transforms them into another (like anime characters), or between objects like handbags and shoes, outputting new, translated images. This tool is designed for those working on advanced computer vision tasks involving generative models and image synthesis.
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