# CrossNet: Latent Cross-Consistency for Unpaired Image Translation

## TL;DR

CrossNet introduces a novel architecture with latent cross-consistency constraints that significantly improve unpaired image translation performance by leveraging cross-translators between GANs' latent spaces.

## Contribution

The paper proposes a new architecture with latent cross-consistency regularizers, enhancing unpaired image translation beyond existing methods.

## Key findings

- Outperforms state-of-the-art on multiple image translation tasks
- Effective regularizers improve translation quality
- Latent cross-consistency enhances model stability and results

## Abstract

Recent GAN-based architectures have been able to deliver impressive performance on the general task of image-to-image translation. In particular, it was shown that a wide variety of image translation operators may be learned from two image sets, containing images from two different domains, without establishing an explicit pairing between the images. This was made possible by introducing clever regularizers to overcome the under-constrained nature of the unpaired translation problem. In this work, we introduce a novel architecture for unpaired image translation, and explore several new regularizers enabled by it. Specifically, our architecture comprises a pair of GANs, as well as a pair of translators between their respective latent spaces. These cross-translators enable us to impose several regularizing constraints on the learnt image translation operator, collectively referred to as latent cross-consistency. Our results show that our proposed architecture and latent cross-consistency constraints are able to outperform the existing state-of-the-art on a variety of image translation tasks.