Bayesian Cycle-Consistent Generative Adversarial Networks via Marginalizing Latent Sampling
Bayesian Cycle-Consistent Generative Adversarial Networks via Marginalizing Latent Sampling
Recent techniques built on generative adversarial networks (GANs), such as cycle-consistent GANs, are able to learn mappings among different domains built from unpaired data sets, 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 …