Interpretable Distribution Shift Detection using Optimal Transport

We propose a method to identify and characterize distribution shifts in classification datasets based on optimal transport. It allows the user to identify the extent to which each class is affected by the shift, and retrieves corresponding pairs of samples to provide insights on its nature. We illus...

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Bibliographic Details
Published in:arXiv.org 2022-08
Main Authors: Hulkund, Neha, Fusi, Nicolo, Jennifer Wortman Vaughan, Alvarez-Melis, David
Format: Article
Language:English
Subjects:
Identification methods
Online Access:Get full text
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Summary:We propose a method to identify and characterize distribution shifts in classification datasets based on optimal transport. It allows the user to identify the extent to which each class is affected by the shift, and retrieves corresponding pairs of samples to provide insights on its nature. We illustrate its use on synthetic and natural shift examples. While the results we present are preliminary, we hope that this inspires future work on interpretable methods for analyzing distribution shifts.
ISSN:2331-8422