  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.
