# Reversible GANs for Memory-efficient Image-to-Image Translation

## TL;DR

This paper introduces approximately invertible architectures for image-to-image translation that are memory-efficient, enabling deeper models and achieving superior results on benchmark datasets.

## Contribution

It proposes invertible architectures that are inherently cycle-consistent and memory-efficient, allowing for deeper networks and improved translation quality.

## Key findings

- Superior quantitative results on Cityscapes and Maps datasets
- Models are approximately invertible by design, ensuring cycle-consistency
- Constant memory complexity enables arbitrarily deep architectures

## Abstract

The Pix2pix and CycleGAN losses have vastly improved the qualitative and quantitative visual quality of results in image-to-image translation tasks. We extend this framework by exploring approximately invertible architectures which are well suited to these losses. These architectures are approximately invertible by design and thus partially satisfy cycle-consistency before training even begins. Furthermore, since invertible architectures have constant memory complexity in depth, these models can be built arbitrarily deep. We are able to demonstrate superior quantitative output on the Cityscapes and Maps datasets at near constant memory budget.