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Simultaneous multi-attribute image-to-image translation using parallel latent transform networks

Xu, Sen-Zhe and Lai, Yu-Kun ORCID: 2020. Simultaneous multi-attribute image-to-image translation using parallel latent transform networks. Computer Graphics Forum 39 (7) , pp. 531-542. 10.1111/cgf.14165

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Image‐to‐image translation has been widely studied. Since real‐world images can often be described by multiple attributes, it is useful to manipulate them at the same time. However, most methods focus on transforming between two domains, and when they chain multiple single attribute transform networks together, the results are affected by the order of chaining, and the performance drops with the out‐of‐domain issue for intermediate results. Existing multi‐domain transfer methods mostly manipulate multiple attributes by adding a list of attribute labels to the network feature, but they also suffer from interference of different attributes, and perform worse when multiple attributes are manipulated. We propose a novel approach to multi‐attribute image‐to‐image translation using several parallel latent transform networks, where multiple attributes are manipulated in parallel and simultaneously, which eliminates both issues. To avoid the interference of different attributes, we introduce a novel soft independence constraint for the changes caused by different attributes. Extensive experiments show that our method outperforms state‐of‐the‐art methods.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Publisher: Wiley
ISSN: 0167-7055
Date of First Compliant Deposit: 9 October 2020
Date of Acceptance: 16 September 2020
Last Modified: 07 Nov 2023 16:47

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