Nuisances via Negativa: Adjusting for Spurious Correlations via Data Augmentation
Abstract: In prediction tasks, there exist features that are related to the label in the same way across different settings for that task; these are semantic features or semantics. Features with vary- ing relationships to the label are nuisances. For example, in detecting cows from natural images, the shape of the head is semantic, but because images of cows often have grass back- grounds but not always, the background is a nuisance. Models that exploit nuisance-label relationships face performance degradation when these relationships change. Building mod- els robust to such changes requires additional knowledge beyond samples of the features and labels. For example, existing work uses annotations of nuisances or assumes erm-trained models depend on nuisances. Approaches to integrate new kinds of additional knowledge enlarge the settings where robust models can be built. We develop an approach to use knowledge about the semantics via data augmentations. These data augmentations cor- rupt semantic information to produce models that identify and adjust for where nuisances drive predictions. We study semantic corruptions in powering different spurious-correlation- avoiding methods on multiple out-of-distribution (ood) tasks like classifying waterbirds, natural language inference (nli), and detecting cardiomegaly in chest X-rays.
Submission Length: Regular submission (no more than 12 pages of main content)
Assigned Action Editor: ~Stefan_Lee1
Submission Number: 1964
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