论文标题
用于无监督异常定位的多尺度特征模仿
Multi-scale Feature Imitation for Unsupervised Anomaly Localization
论文作者
论文摘要
无监督的异常本地化任务面临缺失异常样品训练，检测多种异常的挑战，并处理了多个异常区域的比例。提出了一个单独的教师特征模仿网络结构和一种结合图像和特征金字塔的多规模处理策略来解决这些问题。提出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。实验结果表明，所提出的算法在同一时期对实际工业产品检测数据集上的特征建模异常定位方法的性能更好。与基准方法相比，多尺度策略可以有效地改善效果。
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.
