  The use of deep features coming from pre-trained neural networks for
unsupervised anomaly detection purposes has recently gathered momentum in the
computer vision field. In particular, industrial inspection applications can
take advantage of such features, as demonstrated by the multiple successes of
related methods on the MVTec Anomaly Detection (MVTec AD) dataset. These
methods make use of neural networks pre-trained on auxiliary classification
tasks such as ImageNet. However, to our knowledge, no comparative study of
robustness to the low data regimes between these approaches has been conducted
yet. For quality inspection applications, the handling of limited sample sizes
may be crucial as large quantities of images are not available for small
series. In this work, we aim to compare three approaches based on deep
pre-trained features when varying the quantity of available data in MVTec AD:
KNN, Mahalanobis, and PaDiM. We show that although these methods are mostly
robust to small sample sizes, they still can benefit greatly from using data
augmentation in the original image space, which allows to deal with very small
production runs.
