{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T23:37:40Z","timestamp":1768347460695,"version":"3.49.0"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"abstract":"<jats:p>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, as demonstrated in previous work, these methods struggle to detect OOD inputs that share nuisance values (e.g. background) with in-distribution inputs. The detection of shared-nuisance OOD (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 these failures and propose nuisance-aware OOD detection to address them. Nuisance-aware OOD detection substitutes a classifier trained via Empirical Risk Minimization (ERM) with one that 1. approximates 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.<\/jats:p>","DOI":"10.1609\/aaai.v37i12.26785","type":"journal-article","created":{"date-parts":[[2023,6,27]],"date-time":"2023-06-27T18:29:20Z","timestamp":1687890560000},"page":"15305-15312","source":"Crossref","is-referenced-by-count":2,"title":["Robustness to Spurious Correlations Improves Semantic Out-of-Distribution Detection"],"prefix":"10.1609","volume":"37","author":[{"given":"Lily H.","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Rajesh","family":"Ranganath","sequence":"additional","affiliation":[]}],"member":"9382","published-online":{"date-parts":[[2023,6,26]]},"container-title":["Proceedings of the AAAI Conference on Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/26785\/26557","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/26785\/26557","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,27]],"date-time":"2023-06-27T18:29:20Z","timestamp":1687890560000},"score":1,"resource":{"primary":{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/26785"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,26]]},"references-count":0,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2023,6,27]]}},"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1609\/aaai.v37i12.26785","relation":{},"ISSN":["2374-3468","2159-5399"],"issn-type":[{"value":"2374-3468","type":"electronic"},{"value":"2159-5399","type":"print"}],"subject":[],"published":{"date-parts":[[2023,6,26]]}}}