The simulation visualises CycleGAN's dual-generator, dual-discriminator architecture and shows how an image translated from domain A to B and back to A is compared against the original to compute cycle-consistency loss.
Select a domain pair to translate between, use the speed slider to control how fast the training loop animates, and use play/pause and rebuild to run or reset the cycle-consistency demonstration.
Domain pair select, speed slider, play/pause, rebuild
CycleGAN was published in 2017 by Zhu, Park, Isola, and Efros, and its horse-to-zebra transformation demo became one of the most widely recognised images in generative AI research.
The simulation visualises CycleGAN's dual-generator, dual-discriminator architecture and shows how an image translated from domain A to B and back to A is compared against the original to compute cycle-consistency loss.
The simulation visualises CycleGAN's dual-generator, dual-discriminator architecture and shows how an image translated from domain A to B and back to A is compared against the original to compute cycle-consistency loss.
Select a domain pair to translate between, use the speed slider to control how fast the training loop animates, and use play/pause and rebuild to run or reset the cycle-consistency demonstration.
CycleGAN was published in 2017 by Zhu, Park, Isola, and Efros, and its horse-to-zebra transformation demo became one of the most widely recognised images in generative AI research.