Anomaly detection has recently seen great progress in the field of visual
inspection. More specifically, the use of classical outlier detection
techniques on features extracted by deep pre-trained neural networks have been
shown to deliver remarkable performances on the MVTec Anomaly Detection (MVTec
AD) dataset. However, like most other anomaly detection strategies, these
pre-trained methods assume all training data to be normal. As a consequence,
they cannot be considered as fully unsupervised. There exists to our knowledge
no work studying these pre-trained methods under fully unsupervised setting. In
this work, we first assess the robustness of these pre-trained methods to fully
unsupervised context, using polluted training sets (i.e. containing defective
samples), and show that these methods are more robust to pollution compared to
methods such as CutPaste. We then propose SROC, a Simple Refinement strategy
for One Class classification. SROC enables to remove most of the polluted
images from the training set, and to recover some of the lost AUC. We further
show that our simple heuristic competes with, and even outperforms much more
complex strategies from the existing literature.

本文研究使用深度预训练神经网络提取特征的传统离群值检测技术在 MVTec Anomaly Detection 数据集上交出的出色成绩在全无监督场景下的鲁棒性，提出了一种简单的单类分类策略（SROC），并证明其在与现有文献中更复杂的策略竞争时具有更好的性能。