mlsanityData to 7 Oct 2026

Separating Content and Style for Unsupervised Image-to-Image Translation

arXiv 2110.14404 ↗First posted 27 Oct 2021cs.CVcited by 1

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Abstract (arXiv)

Unsupervised image-to-image translation aims to learn the mapping between two visual domains with unpaired samples. Existing works focus on disentangling domain-invariant content code and domain-specific style code individually for multimodal purposes. However, less attention has been paid to interpreting and manipulating the translated image. In this paper, we propose to separate the content code and style code simultaneously in a unified framework. Based on the correlation between the latent features and the high-level domain-invariant tasks, the proposed framework demonstrates superior performance in multimodal translation, interpretability and manipulation of the translated image. Experimental results show that the proposed approach outperforms the existing unsupervised image translation methods in terms of visual quality and diversity.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 51 of its 60 references that are in the map. First posted on arXiv 27 Oct 2021.

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51 of 60 references.

References · 51

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38 more
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Citing papers per year: 2024: 1.

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  1. 2024An Analysis for Image-to-Image Translation and Style Transfercited by 1