The unsupervised anomaly localization task faces the challenge of missing anomaly sample training, detecting multiple types of anomalies, and dealing with the proportion of the area of multiple anomalies. A separate teacher-student feature imitation network structure and a multi-scale processing strategy combining an image and feature pyramid are proposed to solve these problems. A network module importance search method based on gradient descent optimization is proposed to simplify the network structure. The experimental results show that the proposed algorithm performs better than the feature modeling anomaly localization method on the real industrial product detection dataset in the same period. The multi-scale strategy can effectively improve the effect compared with the benchmark method.

翻译：未经监督的异常点定位任务面临着缺少异常点样本培训、发现多种异常类型和处理多种异常地区比例的挑战。为了解决这些问题,建议了单独的教师-学生特征模拟网络结构和将图像和特征金字塔相结合的多尺度处理战略。建议了基于梯度下降优化的网络模块重要性搜索方法以简化网络结构。实验结果显示,拟议的算法在同一时期比实际工业产品检测数据集的异常点定位方法模型效果更好。与基准方法相比,多尺度战略可以有效改善效果。