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Unraveling the Key Components of OOD Generalization via Diversification
April 23, 2024, 4:44 a.m. | Harold Benoit, Liangze Jiang, Andrei Atanov, O\u{g}uzhan Fatih Kar, Mattia Rigotti, Amir Zamir
cs.LG updates on arXiv.org arxiv.org
Abstract: Supervised learning datasets may contain multiple cues that explain the training set equally well, i.e., learning any of them would lead to the correct predictions on the training data. However, many of them can be spurious, i.e., lose their predictive power under a distribution shift and consequently fail to generalize to out-of-distribution (OOD) data. Recently developed "diversification" methods (Lee et al., 2023; Pagliardini et al., 2023) approach this problem by finding multiple diverse hypotheses that …
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