Study for Identity Losses in Image-to-Image Domain Translation with Cycle-Consistent Generative Adversarial Network

The effect of identity loss in image-to-image domain translation using generative adversarial networks (GAN) remains largely uncertain given its wide application in many successful GAN architectures, e.g., CycleGAN. In this study, negative identity regularization and idempotence regularization are identified from several related studies, and a novel loss, positive identity loss which limits the generative model’s output on the input of positive samples from the source domain, is defined. Results from identity regularized GANs and those without are compared and analyzed. This study concludes that: 1) negative identity loss is helpful in regularizing CycleGAN’s tendency to apply inversible but non-identity transform on non-features such as coloration and background landscapes; 2) positive identity loss significantly increases resolution, details, and affinity with the original image on top of negative identity loss; 3) idempotence loss has positive but limited impacts on output image quality.