ID and OOD Performance Are Sometimes Inversely Correlated on Real-world Datasets

• URL: http://arxiv.org/abs/2209.00613v4
• Date: Fri, 19 May 2023 07:24:53 GMT
• Title: ID and OOD Performance Are Sometimes Inversely Correlated on Real-world Datasets
• Authors: Damien Teney, Yong Lin, Seong Joon Oh, Ehsan Abbasnejad
• Abstract summary: In-distribution (ID) and out-of-distribution (OOD) performance of models in computer vision and NLP are compared. Some studies report a frequent positive correlation and some surprisingly never observe an inverse correlation indicative of a necessary trade-off. This paper shows with multiple datasets that inverse correlations between ID and OOD performance do happen in real-world data.
• Score: 30.82918381331854
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Several studies have compared the in-distribution (ID) and out-of-distribution (OOD) performance of models in computer vision and NLP. They report a frequent positive correlation and some surprisingly never even observe an inverse correlation indicative of a necessary trade-off. The possibility of inverse patterns is important to determine whether ID performance can serve as a proxy for OOD generalization capabilities. This paper shows with multiple datasets that inverse correlations between ID and OOD performance do happen in real-world data - not only in theoretical worst-case settings. We also explain theoretically how these cases can arise even in a minimal linear setting, and why past studies could miss such cases due to a biased selection of models. Our observations lead to recommendations that contradict those found in much of the current literature. - High OOD performance sometimes requires trading off ID performance. - Focusing on ID performance alone may not lead to optimal OOD performance. It may produce diminishing (eventually negative) returns in OOD performance. - In these cases, studies on OOD generalization that use ID performance for model selection (a common recommended practice) will necessarily miss the best-performing models, making these studies blind to a whole range of phenomena.

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