  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.
