On the Impact of Spurious Correlation for Out-of-distribution Detection

Yifei, Ming,; Yin, H.; Li, Yixuan

doi:10.48550/arxiv.2109.05642

Cited by 2 publications

(2 citation statements)

References 21 publications

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“…Attributes such as gender in the mentioned example are called protected variables. In OOD detection literature, a recent work [148] systematically investigates how spurious correlation in the training set impacts OOD detection. The results suggest that the OOD detection performance is severely worsened when the correlation between spurious features and labels is increased in the training set.…”

Section: Fairness and Biases Of Modelsmentioning

confidence: 99%

A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges

Salehi¹,

Mirzaei²,

Hendrycks³

et al. 2021

Preprint

Self Cite

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Machine learning models often encounter samples that are diverged from the training distribution. Failure to recognize an out-of-distribution (OOD) sample, and consequently assign that sample to an in-class label significantly compromises the reliability of a model. The problem has gained significant attention due to its importance for safety deploying models in open-world settings. Detecting OOD samples is challenging due to the intractability of modeling all possible unknown distributions. To date, several research domains tackle the problem of detecting unfamiliar samples, including anomaly detection, novelty detection, one-class learning, open set recognition, and out-of-distribution detection. Despite having similar and shared concepts, out-of-distribution, open-set, and anomaly detection have been investigated independently. Accordingly, these research avenues have not cross-pollinated, creating research barriers. While some surveys intend to provide an overview of these approaches, they seem to only focus on a specific domain without examining the relationship between different domains. This survey aims to provide a cross-domain and comprehensive review of numerous eminent works in respective areas while identifying their commonalities. Researchers can benefit from the overview of research advances in different fields and develop future methodology synergistically. Furthermore, to the best of our knowledge, while there are surveys in anomaly detection or one-class learning, there is no comprehensive or up-to-date survey on out-of-distribution detection, which our survey covers extensively. Finally, having a unified cross-domain perspective, we discuss and shed light on future lines of research, intending to bring these fields closer together.

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Section: Fairness and Biases Of Modelsmentioning

confidence: 99%

A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges

Salehi¹,

Mirzaei²,

Hendrycks³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…We also provide a more fine-grained categorization of distributions for the purpose of thoroughly evaluating an algorithm. Specifically, we divide distributions into four groups: training ID, covariate-shifted ID, near-OOD, and far-OOD (the latter two are inspired by a recent study [10]). Figure 1-a shows example images from the DIGITS benchmark: the covariate-shifted images contain the same semantics as the training images, i.e., digits from 0 to 9, and should be classified as ID, whereas the two OOD groups clearly differ in semantics but represent two different levels of covariate shift.…”

Section: Introductionmentioning

confidence: 99%

Full-Spectrum Out-of-Distribution Detection

Yang¹,

Zhou²,

Liu³

2022

Preprint

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Existing out-of-distribution (OOD) detection literature clearly defines semantic shift as a sign of OOD but does not have a consensus over covariate shift. Samples experiencing covariate shift but not semantic shift are either excluded from the test set or treated as OOD, which contradicts the primary goal in machine learning-being able to generalize beyond the training distribution. In this paper, we take into account both shift types and introduce full-spectrum OOD (FS-OOD) detection, a more realistic problem setting that considers both detecting semantic shift and being tolerant to covariate shift; and designs three benchmarks. These new benchmarks have a more finegrained categorization of distributions (i.e., training ID, covariate-shifted ID, near-OOD, and far-OOD) for the purpose of more comprehensively evaluating the pros and cons of algorithms. To address the FS-OOD detection problem, we propose SEM, a simple feature-based semantics score function. SEM is mainly composed of two probability measures: one is based on high-level features containing both semantic and non-semantic information, while the other is based on low-level feature statistics only capturing non-semantic image styles. With a simple combination, the non-semantic part is cancelled out, which leaves only semantic information in SEM that can better handle FS-OOD detection. Extensive experiments on the three new benchmarks show

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On the Impact of Spurious Correlation for Out-of-distribution Detection

Cited by 2 publications

References 21 publications

A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges

A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges

Full-Spectrum Out-of-Distribution Detection

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