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 this https URL

使用无监督的异常检测方法，我们展示出仅使用极少量的训练样本即可达到甚至超过整个训练数据集的异常检测性能，并提出了三种方法来识别大规模样本中的原型样本。这些原型样本在多个数据集上具有很好的性能，其特征可用于不同模型和其他数据集的选择过程。