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

Exploring Patch-wise Semantic Relation for Contrastive Learning in Image-to-Image Translation Tasks