Debugging and Explaining Metric Learning Approaches: An Influence Function Based Perspective

Deep metric learning (DML) learns a generalizable embedding space where the representations of semantically similar samples are closer. Despite achieving good performance, the state-of-the-art models still suffer from the generalization errors such as farther similar samples and closer dissimilar samples in the space. EIF is designed to efficiently identify and quantify how a subset of training samples contributes to the generalization errors. Moreover, given a user-specific error, EIF can be used to relabel a potentially noisy training sample as mitigation. In our quantitative experiment, EIF outperforms the traditional baseline in identifying more relevant training samples with statistical significance and 33.5% less time.