Journal
IEEE ACCESS
Volume 11, Issue -, Pages 120728-120740Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2023.3324044
Keywords
Set-to-set matching; permutation-invariant; machine learning; distribution shift; covariate shift
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This study refines and redefines the covariate shift assumption in set matching and analyzes the performance of models under these conditions.
The task of set matching, which models the quality of matching between pairs of sets, is expected to have a wide range of practical applications. However, many existing methods that address this task assume that the training and testing distributions are identical, which is frequently violated in real-world scenarios. To address this issue, the covariate shift assumption focuses on the shift in the distribution of covariates between the training and testing datasets. While several studies have analyzed this assumption for vector inputs, there is a lack of research on similar assumptions when the input is a pair of sets. In this study, we refine and redefine the covariate shift assumption in set matching and analyze how models perform under these conditions.
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