How Low Can You Go? Surfacing Prototypical In-Distribution Samples for
  Unsupervised Anomaly Detection
Felix Meissen Johannes Getzner Alexander Ziller Georgios Kaissis Daniel Rueckert
Unsupervised anomaly detection (UAD) alleviates large labeling efforts by
training exclusively on unlabeled in-distribution data and detecting outliers
as anomalies. Generally, the assumption prevails that large training datasets
allow the training of higher-performing UAD models. However, in this work, we
show that using only very few training samples can already match - and in some
cases even improve - anomaly detection compared to training with the whole
training dataset. We propose three methods to identify prototypical samples
from a large dataset of in-distribution samples. We demonstrate that by
training with a subset of just ten such samples, we achieve an area under the
receiver operating characteristics curve (AUROC) of $96.37 \%$ on CIFAR10,
$92.59 \%$ on CIFAR100, $95.37 \%$ on MNIST, $95.38 \%$ on Fashion-MNIST,
$96.37 \%$ on MVTec-AD, $98.81 \%$ on BraTS, and $81.95 \%$ on RSNA pneumonia
detection, even exceeding the performance of full training in $25/67$ classes
we tested. Additionally, we show that the prototypical in-distribution samples
identified by our proposed methods translate well to different models and other
datasets and that using their characteristics as guidance allows for successful
manual selection of small subsets of high-performing samples. Our code is
available at https://anonymous.4open.science/r/uad_prototypical_samples/