UVCGAN: UNet Vision Transformer cycle-consistent GAN for unpaired image-to-image translation

Understand

Unpaired image-to-image translation has broad applications in art, design, and scientific simulations.

• One early breakthrough was CycleGAN that emphasizes one-to-one mappings between two unpaired image domains via generative-adversarial networks (GAN) coupled with the cycle-consistency constraint, while more recent works promote one-to-many mapping to boost diversity of the translated images.
• Motivated by scientific simulation and one-to-one needs, this work revisits the classic CycleGAN framework and boosts its performance to outperform more contemporary models without relaxing the cycle-consistency constraint.
• To achieve this, we equip the generator with a Vision Transformer (ViT) and employ necessary training and regularization techniques.