论文标题
CFA：基于偶联的基于 - 高剂的特征适应针对目标异常定位的特征
CFA: Coupled-hypersphere-based Feature Adaptation for Target-Oriented Anomaly Localization
论文作者
论文摘要
长期以来，在行业中广泛使用异常定位。先前的研究集中在近似于正常特征的分布而不适应目标数据集的情况下。但是，由于异常定位应精确区分正常和异常特征，因此缺乏适应性可能会使异常特征的正态性高估。因此，我们建议使用适合目标数据集的功能来完成基于耦合的 - 伴随性特征适应（CFA）。 CFA由（1）一个可学习的补丁描述符组成，该描述符可学习和嵌入面向目标的功能，以及（2）可扩展的内存库，独立于目标数据集的大小。并且，CFA采用转移学习以增加正常特征密度，因此可以通过将贴片描述符和记忆库应用于预训练的CNN来清楚地区分异常特征。所提出的方法在定量和质量上优于先前的方法。例如，它提供的AUROC分数为99.5％，在MVTEC AD基准测试的异常定位中提供98.5％。此外，本文指出了预训练的CNN的偏见特征的负面影响，并强调适应目标数据集的重要性。该代码可在https://github.com/sungwool/cfa_for_anomaly_localization上公开获得。
For a long time, anomaly localization has been widely used in industries. Previous studies focused on approximating the distribution of normal features without adaptation to a target dataset. However, since anomaly localization should precisely discriminate normal and abnormal features, the absence of adaptation may make the normality of abnormal features overestimated. Thus, we propose Coupled-hypersphere-based Feature Adaptation (CFA) which accomplishes sophisticated anomaly localization using features adapted to the target dataset. CFA consists of (1) a learnable patch descriptor that learns and embeds target-oriented features and (2) scalable memory bank independent of the size of the target dataset. And, CFA adopts transfer learning to increase the normal feature density so that abnormal features can be clearly distinguished by applying patch descriptor and memory bank to a pre-trained CNN. The proposed method outperforms the previous methods quantitatively and qualitatively. For example, it provides an AUROC score of 99.5% in anomaly detection and 98.5% in anomaly localization of MVTec AD benchmark. In addition, this paper points out the negative effects of biased features of pre-trained CNNs and emphasizes the importance of the adaptation to the target dataset. The code is publicly available at https://github.com/sungwool/CFA_for_anomaly_localization.
