Generalized Out-of-Distribution Detection¶
Yang, J., Zhou, K., Li, Y., & Liu, Z. (2021/2024). Generalized Out-of-Distribution Detection: A Survey. International Journal of Computer Vision, 5635-5662.
Cited by¶
1 citation across 1 artifact.
Primes¶
- Out Of Distribution Detection
- Machine learning: explicit detectors in classifiers (energy scores, Mahalanobis distance, deep ensembles), open-set recognition, selective prediction with reject options, abstention in conformal prediction.
Supported in partVerified against the publisher's abstract
The survey's abstract ties OOD detection to open-set recognition and to classification-, density- and distance-based methods, but names none of the specific detectors the claim enumerates.
“several other problems, including anomaly detection (AD), novelty detection (ND), open set recognition (OSR), and outlier detection (OD), are closely related to OOD detection in terms of motivation and methodology”
- Machine learning: explicit detectors in classifiers (energy scores, Mahalanobis distance, deep ensembles), open-set recognition, selective prediction with reject options, abstention in conformal prediction.
Verification¶
Does it exist? Confirmed. This work's DOI resolves to a registered record, which fixes its identity. That is all it fixes.
Does it back the claim? Read against the text for 1 of 1 citation: 1 supported in part. Each verdict is shown under its citation below, with what in the work backs the sentence.
Support is checked per citation rather than per work — the same source can be cited soundly in one article and wrongly in another. Per-citation recording began recently, so a citation with no recorded check is a gap in the record rather than evidence it went unchecked.
See how references were verified.
Registry ID ref:4fbbb2b46906 · see in the full table