Cycle GAN

CycleGANs are generative adversarial networks designed for image-to-image translation, particularly useful when paired training data is scarce or unavailable. Current research focuses on improving CycleGAN architectures through modifications like incorporating attention mechanisms, structural information, and semi-supervised learning to enhance image quality and address issues such as "steganography" and domain shift. These advancements are impacting diverse fields, enabling applications such as underwater image enhancement, medical image analysis (including virtual staining and domain adaptation), and face recognition, by improving image quality and facilitating tasks like segmentation and super-resolution.