Many unsupervised visual anomaly detection methods train an auto-encoder to reconstruct normal samples and then leverage the reconstruction error map to detect and localize the anomalies. However, due to the powerful modeling and generalization ability of neural networks, some anomalies can also be well reconstructed, resulting in unsatisfactory detection and localization accuracy. In this paper, a small coarsely-labeled anomaly dataset is first collected. Then, a coarse-knowledge-aware adversarial learning method is developed to align the distribution of reconstructed features with that of normal features. The alignment can effectively suppress the auto-encoder's reconstruction ability on anomalies and thus improve the detection accuracy. Considering that anomalies often only occupy very small areas in anomalous images, a patch-level adversarial learning strategy is further developed. Although no patch-level anomalous information is available, we rigorously prove that by simply viewing any patch features from anomalous images as anomalies, the proposed knowledge-aware method can also align the distribution of reconstructed patch features with the normal ones. Experimental results on four medical datasets and two industrial datasets demonstrate the effectiveness of our method in improving the detection and localization performance.

翻译：许多无监督视觉异常检测方法通过训练自编码器重构正常样本，并利用重构误差图进行异常检测与定位。然而，由于神经网络强大的建模与泛化能力，部分异常样本同样可能被准确重构，导致检测与定位精度不尽如人意。本文首先收集了一个小型粗标注异常数据集，随后提出一种粗粒度知识感知的对抗学习方法，以对齐重构特征与正常特征的分布。该对齐机制能有效抑制自编码器对异常样本的重构能力，从而提升检测精度。考虑到异常区域在异常图像中往往仅占极小面积，本文进一步提出块级对抗学习策略。尽管未获得块级异常标注信息，我们严格证明：仅需将异常图像中的任意块特征视为异常，所提出的知识感知方法同样能实现重构块特征分布与正常块分布的对齐。在四个医学数据集与两个工业数据集上的实验结果表明，该方法能有效提升异常检测与定位性能。