Bayesian CycleGAN via Marginalizing Latent Sampling

Haoran You, Yu Cheng, Tianheng Cheng, Chunliang Li, Pan Zhou

Recent techniques built on Generative Adversarial Networks (GANs) like CycleGAN are able to learn mappings between domains from unpaired datasets through min-max optimization games between generators and discriminators. However, it remains challenging to stabilize training process and diversify generated results. To address these problems, we present a Bayesian extension of cyclic model and an integrated cyclic framework for inter-domain mappings. The proposed method stimulated by Bayesian GAN explores the full posteriors of Bayesian cyclic model (with latent sampling) and optimizes the model with maximum a posteriori (MAP) estimation. Hence, we name it {\tt Bayesian CycleGAN}. We perform the proposed Bayesian CycleGAN on multiple benchmark datasets, including Cityscapes, Maps, and Monet2photo. The quantitative and qualitative evaluations demonstrate the proposed method can achieve more stable training, superior performance and diversified images generating.

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