  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.
