Smoothing the Disentangled Latent Style Space for Unsupervised Image-to-Image Translation

Yahui Liu, Enver Sangineto, Yajing Chen, Linchao Bao, Haoxian Zhang, Nicu Sebe, Bruno Lepri, Wei Wang, Marco De Nadai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Image-to-Image (I2I) multi-domain translation models are usually evaluated also using the quality of their semantic interpolation results. However, state-of-the-art models frequently show abrupt changes in the image appearance during interpolation, and usually perform poorly in interpolations across domains. In this paper, we propose a new training protocol based on three specific losses which help a translation network to learn a smooth and disentangled latent style space in which: 1) Both intra- and inter-domain interpolations correspond to gradual changes in the generated images and 2) The content of the source image is better preserved during the translation. Moreover, we propose a novel evaluation metric to properly measure the smoothness of latent style space of I2I translation models. The proposed method can be plugged in existing translation approaches, and our extensive experiments on different datasets show that it can significantly boost the quality of the generated images and the graduality of the interpolations.
Original languageEnglish
Title of host publication2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
PublisherIEEE
Pages10780-10789
Number of pages10
ISBN (Electronic)9781665445092
ISBN (Print)9781665445108 (PoD)
DOIs
Publication statusPublished - 2 Nov 2021
Event2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) - Nashville, TN, USA
Duration: 20 Jun 202125 Jun 2021

Publication series

NameProceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition
PublisherIEEE
ISSN (Print)1063-6919
ISSN (Electronic)2575-7075

Conference

Conference2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Period20/06/2125/06/21

Keywords

  • Training
  • Interpolation
  • Computer vision
  • Smoothing methods
  • Protocols
  • Computational modeling
  • Semantics

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