HyperMix：少样本设置下的分布外检测与分类
HyperMix: Out-of-Distribution Detection and Classification in Few-Shot Settings
AI总结：
针对少样本OOD检测难题，提出HyperMix超网络框架，利用Mixup和出剧集异常值暴露，在CIFAR-FS和MiniImageNet上显著超越现有方法。
摘要：
分布外（OOD）检测是现实世界机器学习系统中的一个重要课题，但有限分布内样本的设置尚未得到充分探索。这种少样本OOD设置具有挑战性，因为模型在学习数据分布的机会有限的情况下，就要被任务识别OOD样本。事实上，我们证明了最近最先进的OOD方法在少样本设置中未能超越简单的基线方法。因此，我们提出了一个名为HyperMix的超网络框架，在生成的分类器参数上使用Mixup，以及一种自然的出剧集异常值暴露技术，该技术不需要额外的异常值数据集。我们在CIFAR-FS和MiniImageNet上进行了实验，在少样本机制中显著优于其他OOD方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
