Knowledge Distillation-based Anomaly Detection (KDAD) methods rely on the teacher-student paradigm to detect and segment anomalous regions by contrasting the unique features extracted by both networks. However, existing KDAD methods suffer from two main limitations: 1) the student network can effortlessly replicate the teacher network's representations, and 2) the features of the teacher network serve solely as a ``reference standard" and are not fully leveraged. Toward this end, we depart from the established paradigm and instead propose an innovative approach called Asymmetric Distillation Post-Segmentation (ADPS). Our ADPS employs an asymmetric distillation paradigm that takes distinct forms of the same image as the input of the teacher-student networks, driving the student network to learn discriminating representations for anomalous regions. Meanwhile, a customized Weight Mask Block (WMB) is proposed to generate a coarse anomaly localization mask that transfers the distilled knowledge acquired from the asymmetric paradigm to the teacher network. Equipped with WMB, the proposed Post-Segmentation Module (PSM) is able to effectively detect and segment abnormal regions with fine structures and clear boundaries. Experimental results demonstrate that the proposed ADPS outperforms the state-of-the-art methods in detecting and segmenting anomalies. Surprisingly, ADPS significantly improves Average Precision (AP) metric by 9% and 20% on the MVTec AD and KolektorSDD2 datasets, respectively.

翻译：基于知识蒸馏的异常检测方法依赖于师生网络范式，通过对比两个网络提取的独特特征来检测和分割异常区域。然而，现有KDAD方法存在两个主要局限：1）学生网络可以轻易复制教师网络的表征；2）教师网络的特征仅作为"参考标准"而未得到充分利用。为此，我们突破现有范式，提出了一种名为非对称蒸馏后分割的创新方法。ADPS采用非对称蒸馏范式，将同一图像的不同形式作为师生网络的输入，驱动学生网络学习异常区域的判别性表征。同时，我们提出定制的权重掩码模块，用于生成粗粒度异常定位掩码，将从非对称范式中获得的蒸馏知识迁移至教师网络。借助WMB，所提出的后分割模块能够有效检测并分割具有精细结构和清晰边界的异常区域。实验结果表明，所提出的ADPS在异常检测与分割性能上优于现有最优方法。值得注意的是，ADPS在MVTec AD和KolektorSDD2数据集上分别将平均精度指标提升了9%和20%。