Unpaired Image-to-Image Translation With Shortest Path Regularization

Shaoan Xie, Yanwu Xu, Mingming Gong, Kun Zhang; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023, pp. 10177-10187

Abstract


Unpaired image-to-image translation aims to learn proper mappings that can map images from one domain to another domain while preserving the content of the input image. However, with large enough capacities, the network can learn to map the inputs to any random permutation of images in another domain. Existing methods treat two domains as discrete and propose different assumptions to address this problem. In this paper, we start from a different perspective and consider the paths connecting the two domains. We assume that the optimal path length between the input and output image should be the shortest among all possible paths. Based on this assumption, we propose a new method to allow generating images along the path and present a simple way to encourage the network to find the shortest path without pair information. Extensive experiments on various tasks demonstrate the superiority of our approach.

Related Material


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[bibtex]
@InProceedings{Xie_2023_CVPR, author = {Xie, Shaoan and Xu, Yanwu and Gong, Mingming and Zhang, Kun}, title = {Unpaired Image-to-Image Translation With Shortest Path Regularization}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2023}, pages = {10177-10187} }