  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. Compared to previous best-performing models, our model performs
better and retains a strong correlation between the original and translated
image. An accompanying ablation study shows that both the gradient penalty and
self-supervised pre-training are crucial to the improvement. To promote
reproducibility and open science, the source code, hyperparameter
configurations, and pre-trained model are available at
https://github.com/LS4GAN/uvcgan.
