HyperMix: Out-of-Distribution Detection and Classification in Few-Shot Settings
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Abstract (arXiv)
Out-of-distribution (OOD) detection is an important topic for real-world machine learning systems, but settings with limited in-distribution samples have been underexplored. Such few-shot OOD settings are challenging, as models have scarce opportunities to learn the data distribution before being tasked with identifying OOD samples. Indeed, we demonstrate that recent state-of-the-art OOD methods fail to outperform simple baselines in the few-shot setting. We thus propose a hypernetwork framework called HyperMix, using Mixup on the generated classifier parameters, as well as a natural out-of-episode outlier exposure technique that does not require an additional outlier dataset. We conduct experiments on CIFAR-FS and MiniImageNet, significantly outperforming other OOD methods in the few-shot regime.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 47 of its 64 references that are in the map. First posted on arXiv 22 Dec 2023.
Builds on
47 of 64 references.
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35 more
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