A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution
Detection: Solutions and Future Challenges
- URL: http://arxiv.org/abs/2110.14051v1
- Date: Tue, 26 Oct 2021 22:05:31 GMT
- Title: A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution
Detection: Solutions and Future Challenges
- Authors: Mohammadreza Salehi, Hossein Mirzaei, Dan Hendrycks, Yixuan Li,
Mohammad Hossein Rohban, Mohammad Sabokrou
- Abstract summary: Machine learning models often encounter samples that are diverged from the training distribution.
Despite having similar and shared concepts, out-of-distribution, open-set, and anomaly detection have been investigated independently.
This survey aims to provide a cross-domain and comprehensive review of numerous eminent works in respective areas.
- Score: 28.104112546546936
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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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