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 an important topic for real-worldmachine learning systems, but settings with limited in-distribution sampleshave been underexplored. Such few-shot OOD settings are challenging, as modelshave scarce opportunities to learn the data distribution before being taskedwith identifying OOD samples. Indeed, we demonstrate that recentstate-of-the-art OOD methods fail to outperform simple baselines in thefew-shot setting. We thus propose a hypernetwork framework called HyperMix,using Mixup on the generated classifier parameters, as well as a naturalout-of-episode outlier exposure technique that does not require an additionaloutlier dataset. We conduct experiments on CIFAR-FS and MiniImageNet,significantly outperforming other OOD methods in the few-shot regime.

HyperMix: Out-of-Distribution Detection and Classification in Few-Shot Settings