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.
CITATION STYLE
Saito, Y., Nakamura, T., Hachiya, H., & Fukumizu, K. (2020). Exchangeable Deep Neural Networks for Set-to-Set Matching and Learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12362 LNCS, pp. 626–646). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-58520-4_37
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