mlsanityData to 14 Oct 2026

Identifying and Benchmarking Natural Out-of-Context Prediction Problems

arXiv 2110.13223 ↗First posted 25 Oct 2021cs.LG cs.CVcited by 2

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Abstract (arXiv)

Deep learning systems frequently fail at out-of-context (OOC) prediction, the problem of making reliable predictions on uncommon or unusual inputs or subgroups of the training distribution. To this end, a number of benchmarks for measuring OOC performance have recently been introduced. In this work, we introduce a framework unifying the literature on OOC performance measurement, and demonstrate how rich auxiliary information can be leveraged to identify candidate sets of OOC examples in existing datasets. We present NOOCh: a suite of naturally-occurring "challenge sets", and show how varying notions of context can be used to probe specific OOC failure modes. Experimentally, we explore the tradeoffs between various learning approaches on these challenge sets and demonstrate how the choices made in designing OOC benchmarks can yield varying conclusions.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 41 of its 79 references that are in the map. First posted on arXiv 25 Oct 2021.

Builds on

41 of 79 references.

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Cited by year

Citing papers per year: 2022: 1; 2023: 1.

2 papers.

  1. 2023Challenges and Opportunities in Improving Worst-Group Generalization in Presence of Spurious Featurescited by 4
  2. 2022Detecting out-of-context objects using contextual cuescited by 2