Crossing-Domain Generative Adversarial Networks for Unsupervised Multi-Domain Image-to-Image Translation

TL;DR

This paper introduces a novel unsupervised multi-domain image translation framework using GANs, reducing training complexity and resource requirements while enabling translation between any domain pairs without direct training.

Contribution

The authors propose a general multi-domain GAN framework that learns shared high-level features, allowing translation across multiple domains without pairwise training, improving efficiency and scalability.

Findings

Achieves competitive results on various image translation tasks.

Reduces training time and computational resources compared to pairwise methods.

Enables translation between any domain pairs without direct training.

Abstract

State-of-the-art techniques in Generative Adversarial Networks (GANs) have shown remarkable success in image-to-image translation from peer domain X to domain Y using paired image data. However, obtaining abundant paired data is a non-trivial and expensive process in the majority of applications. When there is a need to translate images across n domains, if the training is performed between every two domains, the complexity of the training will increase quadratically. Moreover, training with data from two domains only at a time cannot benefit from data of other domains, which prevents the extraction of more useful features and hinders the progress of this research area. In this work, we propose a general framework for unsupervised image-to-image translation across multiple domains, which can translate images from domain X to any a domain without requiring direct training between the two domains involved in image translation. A byproduct of the framework is the reduction of computing time and computing resources, since it needs less time than training the domains in pairs as is done in state-of-the-art works. Our proposed framework consists of a pair of encoders along with a pair of GANs which learns high-level features across different domains to generate diverse and realistic samples from. Our framework shows competing results on many image-to-image tasks compared with state-of-the-art techniques.