Abstract

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.

BibTex:

@InProceedings{Mehta_2024_WACV,
    author    = {Mehta, Nikhil and Liang, Kevin J. and Huang, Jing and Chu, Fu-Jen and Yin, Li and Hassner, Tal},
    title     = {{HyperMix}: Out-of-Distribution Detection and Classification in Few-Shot Settings},
    booktitle = {Proceedings of the {IEEE/CVF} Winter Conference on Applications of Computer Vision ({WACV})},
    month     = {January},
    year      = {2024},
    pages     = {2410-2420},
    url       = {https://talhassner.github.io/home/publication/2024_WACV}
}

Links:

Paper on CVF