Exchangeable deep neural networks for set-to-set matching and learning

Yuki Saito; Takuma Nakamura; Hirotaka Hachiya; Kenji Fukumizu

doi:10.1007/978-3-030-58520-4_37

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arXiv · 1910.09972

Exchangeable deep neural networks for set-to-set matching and learning

Yuki Saito·Takuma Nakamura·Hirotaka Hachiya·Kenji Fukumizu

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Abstract

Matching two different sets of items, called heterogeneous set-to-set matching problem, has recently received attention as a promising problem. The difficulties are to extract features to match a correct pair of different sets and also preserve two types of exchangeability required for set-to-set matching: the pair of sets, as well as the items in each set, should be exchangeable. In this study, we propose a novel deep learning architecture to address the abovementioned difficulties and also an efficient training framework for set-to-set matching. We evaluate the methods through experiments based on two industrial applications: fashion set recommendation and group re-identification. In these experiments, we show that the proposed method provides significant improvements and results compared with the state-of-the-art methods, thereby validating our architecture for the heterogeneous set matching problem.

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BibTeX RIS

Yuki Saito, Takuma Nakamura, Hirotaka Hachiya, Kenji Fukumizu. 2021-01-28. Exchangeable deep neural networks for set-to-set matching and learning. https://doi.org/10.1007/978-3-030-58520-4_37

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