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...This innovation lets the model learn domain-to-domain translations like turning horses into zebras, changing seasons, or transforming photos into paintings, using only collections of images from each domain. The original implementation (in Torch) has since been complemented by other re-implementations (including in PyTorch), but the core idea remains: unpaired image-to-image translation. Because of its flexibility, CycleGAN has become one of the most widely adopted generative models for domain translation tasks.

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.