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.

在有限的内分布样本情况下，本文提出了一种名为 HyperMix 的超网络框架，使用对生成的分类器参数进行 Mixup 处理，并采用一种自然的 Episode 外部曝光技术来进行离群样本检测，实验证明在少样本情况下，该方法明显优于其他离群检测方法。