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Deep Set-to-Set Matching and Learning
[article]
2019
arXiv
pre-print
Matching two sets of items, called set-to-set matching problem, is being recently raised. The difficulties of set-to-set matching over ordinary data matching lie in the exchangeability in 1) set-feature extraction and 2) set-matching score; the pair of sets and the items in each set should be exchangeable. In this paper, we propose a deep learning architecture for the set-to-set matching that overcomes the above difficulties, including two novel modules: 1) a cross-set transformation and 2)
arXiv:1910.09972v1
fatcat:wn3jvmpbjzcprkmt2k4ql2gmnm