Alleviating Semantics Distortion in Unsupervised Low-Level Image-to-Image Translation via Structure Consistency Constraint

Unsupervised image-to-image (I21) translation aims to learn a domain mapping function that can preserve the semantics of the input images without paired data. However, because the underlying semantics distributions in the source and target domains are often mismatched, current distribution matching-based methods may distort the semantics when matching distributions, resulting in the inconsistency between the input and translated images, which is known as the semantics distortion problem. In this paper, we focus on the low-level I21 translation, where the structure of images is highly related to their semantics. To alleviate semantic distortions in such translation tasks without paired supervision, we propose a novel I21 translation constraint, called Structure Consistency Constraint (SCC), to promote the consistency of image structures by reducing the randomness of color transformation in the translation process. To facilitate estimation and maximization of SCC, we propose an approximate representation of mutual information called relative Squared-loss Mutual Information (rSMI) that enjoys efficient analytic solutions. Our SCC can be easily incorporated into most existing translation models. Quantitative and qualitative comparisons on a range of low-level I21 translation tasks show that translation models with SCC outperform the original models by a significant margin with little additional computational and memory costs.