Recently, contrastive learning-based image translation methods have been
proposed, which contrasts different spatial locations to enhance the spatial
correspondence. However, the methods often ignore the diverse semantic relation
within the images. To address this, here we propose a novel semantic relation
consistency (SRC) regularization along with the decoupled contrastive learning,
which utilize the diverse semantics by focusing on the heterogeneous semantics
between the image patches of a single image. To further improve the
performance, we present a hard negative mining by exploiting the semantic
relation. We verified our method for three tasks: single-modal and multi-modal
image translations, and GAN compression task for image translation.
Experimental results confirmed the state-of-art performance of our method in
all the three tasks.

本文提出了一种新的语义相关性一致性（SRC）正则化方法和解耦的对比度学习方法，利用单个图像的图像块之间的异构语义来关注多样的语义，进一步通过利用语义关系的困难负样本挖掘来提高性能，并验证了我们的方法在单模态和多模态图像翻译以及图像翻译的 GAN 压缩任务中的最先进性能。