Multi‐cropping contrastive learning and domain consistency for unsupervised image‐to‐image translation
Chen Zhao, Weiling Cai, Zheng Yuan, Chengwei Hu · IET Image Processing · 2025
Abstract Recently, unsupervised image‐to‐image (i2i) translation methods based on contrastive learning have achieved state‐of‐the‐art results. However, in previous works, the negatives are sampled from the input image itself, which inspires us to design a data augmentation method to improve the quality of the selected negatives. Moreover, the previous methods only preserve the content consistency via patch‐wise contrastive learning, which ignores the domain consistency between the generated images and the real images of the target domain. This paper proposes a novel unsupervised i2i translation framework based on multi‐cropping contrastive learning and domain consistency, called MCDUT. Specifically, the multi‐cropping views are obtained with the aim of further generating high‐quality negative examples. To constrain the embeddings in the deep feature space, a new domain consistency loss is formulated, which encourages the generated images to be close to the real images. In many i2i translation tasks, this method achieves state‐of‐the‐art results, and the advantages of this method have been proven through extensive comparison experiments and ablation research. The code of MCDUT is available at https://github.com/zhihefang/MCDUT .