Unpaired image-to-image translation aims to find a mapping between the source
domain and the target domain. To alleviate the problem of the lack of
supervised labels for the source images, cycle-consistency based methods have
been proposed for image structure preservation by assuming a reversible
relationship between unpaired images. However, this assumption only uses
limited correspondence between image pairs. Recently, contrastive learning (CL)
has been used to further investigate the image correspondence in unpaired image
translation by using patch-based positive/negative learning. Patch-based
contrastive routines obtain the positives by self-similarity computation and
recognize the rest patches as negatives. This flexible learning paradigm
obtains auxiliary contextualized information at a low cost. As the negatives
own an impressive sample number, with curiosity, we make an investigation based
on a question: are all negatives necessary for feature contrastive learning?
Unlike previous CL approaches that use negatives as much as possible, in this
paper, we study the negatives from an information-theoretic perspective and
introduce a new negative Pruning technology for Unpaired image-to-image
Translation (PUT) by sparsifying and ranking the patches. The proposed
algorithm is efficient, flexible and enables the model to learn essential
information between corresponding patches stably. By putting quality over
quantity, only a few negative patches are required to achieve better results.
Lastly, we validate the superiority, stability, and versatility of our model
through comparative experiments.

本文研究了针对无配对图像的图像翻译技术，并分析了循环一致性与对比学习等方法的不足。作者提出了一种新的基于信息论的负样本剪枝技术，其可以在不影响翻译效果的情况下减少耗时和计算成本。实验证明，该技术比基于大批量负样本的方法更加优越。