  Recent techniques built on Generative Adversarial Networks (GANs), such as
Cycle-Consistent GANs, are able to learn mappings among different domains built
from unpaired datasets, through min-max optimization games between generators
and discriminators. However, it remains challenging to stabilize the training
process and thus cyclic models fall into mode collapse accompanied by the
success of discriminator. To address this problem, we propose an novel Bayesian
cyclic model and an integrated cyclic framework for inter-domain mappings. The
proposed method motivated by Bayesian GAN explores the full posteriors of
cyclic model via sampling latent variables and optimizes the model with maximum
a posteriori (MAP) estimation. Hence, we name it Bayesian CycleGAN. In
addition, original CycleGAN cannot generate diversified results. But it is
feasible for Bayesian framework to diversify generated images by replacing
restricted latent variables in inference process. We evaluate the proposed
Bayesian CycleGAN on multiple benchmark datasets, including Cityscapes, Maps,
and Monet2photo. The proposed method improve the per-pixel accuracy by 15% for
the Cityscapes semantic segmentation task within origin framework and improve
20% within the proposed integrated framework, showing better resilience to
imbalance confrontation. The diversified results of Monet2Photo style transfer
also demonstrate its superiority over original cyclic model. We provide codes
for all of our experiments in https://github.com/ranery/Bayesian-CycleGAN.
