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 Plus annual

Presents the access of premium content as premium feature

Premium Content

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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Out-of-distribution (OOD) detection is crucial to modern deep learningapplications by identifying and alerting about the OOD samples that should notbe tested or used for making predictions. Current OOD detection methods havemade significant progress when in-distribution (ID) and OOD samples are drawnfrom static distributions. However, this can be unrealistic when applied toreal-world systems which often undergo continuous variations and shifts in IDand OOD distributions over time. Therefore, for an effective application inreal-world systems, the development of OOD detection methods that can adapt tothese dynamic and evolving distributions is essential. In this paper, wepropose a novel and more realistic setting called continuously adaptiveout-of-distribution (CAOOD) detection which targets on developing an OODdetection model that enables dynamic and quick adaptation to a new arrivingdistribution, with insufficient ID samples during deployment time. To addressCAOOD, we develop meta OOD learning (MOL) by designing a learning-to-adaptdiagram such that a good initialized OOD detection model is learned during thetraining process. In the testing process, MOL ensures OOD detection performanceover shifting distributions by quickly adapting to new distributions with a fewadaptations. Extensive experiments on several OOD benchmarks endorse theeffectiveness of our method in preserving both ID classification accuracy andOOD detection performance on continuously shifting distributions.

Meta OOD Learning for Continuously Adaptive OOD Detection