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Exchangeable Deep Neural Networks for Set-to-Set Matching and Learning

Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 12362)

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.

Keywords

Set to set matching Deep learning Permutation invariance 

Supplementary material

504472_1_En_37_MOESM1_ESM.pdf (252 kb)
Supplementary material 1 (pdf 252 KB)

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Authors and Affiliations

  1. 1.ZOZO ResearchShibuyaJapan
  2. 2.The Graduate University for Advanced Studies, SOKENDAITachikawaJapan
  3. 3.Wakayama UniversityWakayamaJapan
  4. 4.The Institute of Statistical MathematicsTachikawaJapan

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