English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Machine learning models often encounter samples that are diverged from thetraining distribution. Failure to recognize an out-of-distribution (OOD)sample, and consequently assign that sample to an in-class label significantlycompromises the reliability of a model. The problem has gained significantattention due to its importance for safety deploying models in open-worldsettings. Detecting OOD samples is challenging due to the intractability ofmodeling all possible unknown distributions. To date, several research domainstackle the problem of detecting unfamiliar samples, including anomalydetection, novelty detection, one-class learning, open set recognition, andout-of-distribution detection. Despite having similar and shared concepts,out-of-distribution, open-set, and anomaly detection have been investigatedindependently. Accordingly, these research avenues have not cross-pollinated,creating research barriers. While some surveys intend to provide an overview ofthese approaches, they seem to only focus on a specific domain withoutexamining the relationship between different domains. This survey aims toprovide a cross-domain and comprehensive review of numerous eminent works inrespective areas while identifying their commonalities. Researchers can benefitfrom the overview of research advances in different fields and develop futuremethodology synergistically. Furthermore, to the best of our knowledge, whilethere are surveys in anomaly detection or one-class learning, there is nocomprehensive or up-to-date survey on out-of-distribution detection, which oursurvey covers extensively. Finally, having a unified cross-domain perspective,we discuss and shed light on future lines of research, intending to bring thesefields closer together.

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