Few-shot anomaly detection (AD) is an emerging sub-field of general AD, and tries to distinguish between normal and anomalous data using only few selected samples. While newly proposed few-shot AD methods do compare against pre-existing algorithms developed for the full-shot domain as baselines, they do not dedicatedly optimize them for the few-shot setting. It thus remains unclear if the performance of such pre-existing algorithms can be further improved. We address said question in this work. Specifically, we present a study on the AD/anomaly segmentation (AS) performance of PatchCore, the current state-of-the-art full-shot AD/AS algorithm, in both the few-shot and the many-shot settings. We hypothesize that further performance improvements can be realized by (I) optimizing its various hyperparameters, and by (II) transferring techniques known to improve few-shot supervised learning to the AD domain. Exhaustive experiments on the public VisA and MVTec AD datasets reveal that (I) significant performance improvements can be realized by optimizing hyperparameters such as the underlying feature extractor, and that (II) image-level augmentations can, but are not guaranteed, to improve performance. Based on these findings, we achieve a new state of the art in few-shot AD on VisA, further demonstrating the merit of adapting pre-existing AD/AS methods to the few-shot setting. Last, we identify the investigation of feature extractors with a strong inductive bias as a potential future research direction for (few-shot) AD/AS.

翻译：少样本异常检测是通用异常检测的一个新兴子领域，旨在仅使用少量选定样本区分正常与异常数据。尽管新提出的少样本异常检测方法确实将针对全样本领域开发的现有算法作为基线进行比较，但并未专门针对少样本场景对其进行优化。因此，这些现有算法的性能能否进一步提升尚不明确。本文针对这一问题展开研究。具体而言，我们系统评估了当前先进的全样本异常检测/异常分割算法——PatchCore在少样本与多样本两种场景下的性能表现。我们假设通过两种途径可实现性能提升：（I）优化其各项超参数；（II）将已知能提升少样本监督学习性能的技术迁移至异常检测领域。在公开VisA和MVTec AD数据集上的详尽实验表明：（I）通过优化底层特征提取器等超参数可显著提升性能；（II）图像级数据增强可能（但非必然）改善性能。基于这些发现，我们在VisA数据集上实现了少样本异常检测的最新先进水平，进一步证实了将现有异常检测/异常分割方法适配至少样本场景的价值。最后，我们指出具有强归纳偏置的特征提取器研究是（少样本）异常检测/异常分割领域潜在的未来研究方向。