Contributed Talk: Cycle-Consistent Adversarial Learning as Approximate Bayesian Inference
ICML 2018 Workshop on Theoretical Foundations and Applications of Deep Generative Models (TAGDM), Stockholm.
Cycle-Consistent Adversarial Learning as Approximate Bayesian Inference
We derive cycle-consistent adversarial learning (CycleGAN) as a special case of variational inference in a latent-variable model with implicit priors, establishing a Bayesian ā¦
A Tutorial on Variational Autoencoders with a Concise Keras Implementation
We give an in-depth practical guide to variational autoencoders from a probabilistic perspective.