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CycleGAN — in its original form — is a landmark in deep learning for image-to-image translation without paired data. Rather than requiring matching image pairs between source and target domains (which are often hard or impossible to obtain), CycleGAN learns two mappings — one from domain A to B, and another back from B to A — along with a cycle-consistency loss that encourages the round-trip to reconstruct the original image. This innovation lets the model learn domain-to-domain translations...

Torch5 provides a Matlab-like environment for state-of-the-art machine learning algorithms. This project aims at gathering all contributed packages for Torch5, which are not part of the Torch5 core.