# Cycle-Consistent Adversarial Learning as Approximate Bayesian Inference

arXiv: https://arxiv.org/abs/1806.01771 · HTML: https://mlsanity.com/paper/1806.01771/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2018.

## Built on by (most cited first, 3 of 3)

- [Trustworthy image-to-image translation: evaluating uncertainty calibration in unpaired training scenarios](https://mlsanity.com/paper/2501.17570/) (2025)
- [Cross-Task Consistency Learning Framework for Multi-Task Learning](https://mlsanity.com/paper/2111.14122/) (2021)
- [Bridging GANs and Bayesian Neural Networks via Partial Stochasticity](https://mlsanity.com/paper/2507.00651/) (2025)

## Abstract (arXiv)

We formalize the problem of learning interdomain correspondences in the absence of paired data as Bayesian inference in a latent variable model (LVM), where one seeks the underlying hidden representations of entities from one domain as entities from the other domain. First, we introduce implicit latent variable models, where the prior over hidden representations can be specified flexibly as an implicit distribution. Next, we develop a new variational inference (VI) algorithm for this model based on minimization of the symmetric Kullback-Leibler (KL) divergence between a variational joint and the exact joint distribution. Lastly, we demonstrate that the state-of-the-art cycle-consistent adversarial learning (CYCLEGAN) models can be derived as a special case within our proposed VI framework, thus establishing its connection to approximate Bayesian inference methods.