Robustness to Spurious Correlations Improves Semantic Out-of-Distribution Detection

• URL: http://arxiv.org/abs/2302.04132v1
• Date: Wed, 8 Feb 2023 15:28:33 GMT
• Title: Robustness to Spurious Correlations Improves Semantic Out-of-Distribution Detection
• Authors: Lily H. Zhang and Rajesh Ranganath
• Abstract summary: Methods which utilize the outputs or feature representations of predictive models have emerged as promising approaches for out-of-distribution (OOD) detection of image inputs. We provide a possible explanation for SN-OOD detection failures and propose nuisance-aware OOD detection to address them.
• Score: 24.821151013905865
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Methods which utilize the outputs or feature representations of predictive models have emerged as promising approaches for out-of-distribution (OOD) detection of image inputs. However, these methods struggle to detect OOD inputs that share nuisance values (e.g. background) with in-distribution inputs. The detection of shared-nuisance out-of-distribution (SN-OOD) inputs is particularly relevant in real-world applications, as anomalies and in-distribution inputs tend to be captured in the same settings during deployment. In this work, we provide a possible explanation for SN-OOD detection failures and propose nuisance-aware OOD detection to address them. Nuisance-aware OOD detection substitutes a classifier trained via empirical risk minimization and cross-entropy loss with one that 1. is trained under a distribution where the nuisance-label relationship is broken and 2. yields representations that are independent of the nuisance under this distribution, both marginally and conditioned on the label. We can train a classifier to achieve these objectives using Nuisance-Randomized Distillation (NuRD), an algorithm developed for OOD generalization under spurious correlations. Output- and feature-based nuisance-aware OOD detection perform substantially better than their original counterparts, succeeding even when detection based on domain generalization algorithms fails to improve performance.

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