Multi-cropping Contrastive Learning and Domain Consistency for Unsupervised Image-to-Image Translation

TL;DR

This paper introduces MCDUT, a novel unsupervised image-to-image translation framework that enhances contrastive learning with multi-cropping and domain consistency, achieving state-of-the-art results.

Contribution

The paper proposes a new framework combining multi-cropping contrastive learning and domain consistency loss, along with a dual coordinate attention network for improved image translation.

Findings

Achieves state-of-the-art results on multiple image translation tasks.

Demonstrates the effectiveness of multi-cropping and domain consistency in contrastive learning.

Validates the proposed methods through extensive experiments and ablation studies.

Abstract

Recently, unsupervised image-to-image translation methods based on contrastive learning have achieved state-of-the-art results in many tasks. However, in the previous works, the negatives are sampled from the input image itself, which inspires us to design a data augmentation method to improve the quality of the selected negatives. Moreover, the previous methods only preserve the content consistency via patch-wise contrastive learning in the embedding space, which ignores the domain consistency between the generated images and the real images of the target domain. In this paper, we propose a novel unsupervised image-to-image translation framework based on multi-cropping contrastive learning and domain consistency, called MCDUT. Specifically, we obtain the multi-cropping views via the center-cropping and the random-cropping with the aim of further generating the high-quality negative examples. To constrain the embeddings in the deep feature space, we formulate a new domain consistency loss, which encourages the generated images to be close to the real images in the embedding space of the same domain. Furthermore, we present a dual coordinate attention network by embedding positional information into the channel, which called DCA. We employ the DCA network in the design of generator, which makes the generator capture the horizontal and vertical global information of dependency. In many image-to-image translation tasks, our method achieves state-of-the-art results, and the advantages of our method have been proven through extensive comparison experiments and ablation research.