Diversify and Disambiguate: Out-of-Distribution Robustness via DisagreementDownload PDF

Published: 01 Feb 2023, Last Modified: 21 Feb 2023ICLR 2023 posterReaders: Everyone
Keywords: Out-of-distribution robustness, spurious correlations, underspecification, ambiguity, ensembles
TL;DR: Given underspecified data, (1) find a diverse set of solutions and (2) choose the best one.
Abstract: Real-world machine learning problems often exhibit shifts between the source and target distributions, in which source data does not fully convey the desired behavior on target inputs. Different functions that achieve near-perfect source accuracy can make differing predictions on test inputs, and such ambiguity makes robustness to distribution shifts challenging. We propose DivDis, a simple two-stage framework for identifying and resolving ambiguity in data. DivDis first learns a diverse set of hypotheses that achieve low source loss but make differing predictions on target inputs. We then disambiguate by selecting one of the discovered functions using additional information, for example, a small number of target labels. Our experimental evaluation shows improved performance in subpopulation shift and domain generalization settings, demonstrating that DivDis can scalably adapt to distribution shifts in image and text classification benchmarks.
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Please Choose The Closest Area That Your Submission Falls Into: Deep Learning and representational learning
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