Neural networks face significant challenges in generalizing to out-of-distribution (OOD) data that deviate from in-distribution (ID) training data. This generalization problem causes serious reliability issues in real-world machine learning applications. Recent research has revealed interesting heuristics that describe model behavior across distribution-shift benchmarks, particularly the “accuracy on the line” (ACL) and “agreement on the line” [...]
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