  Unsupervised image-to-image (I2I) translation learns cross-domain image
mapping that transfers input from the source domain to output in the target
domain while preserving its semantics. One challenge is that different semantic
statistics in source and target domains result in content discrepancy known as
semantic distortion. To address this problem, a novel I2I method that maintains
semantic consistency in translation is proposed and named SemST in this work.
SemST reduces semantic distortion by employing contrastive learning and
aligning the structural and textural properties of input and output by
maximizing their mutual information. Furthermore, a multi-scale approach is
introduced to enhance translation performance, thereby enabling the
applicability of SemST to domain adaptation in high-resolution images.
Experiments show that SemST effectively mitigates semantic distortion and
achieves state-of-the-art performance. Also, the application of SemST to domain
adaptation (DA) is explored. It is demonstrated by preliminary experiments that
SemST can be utilized as a beneficial pre-training for the semantic
segmentation task.
