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.

翻译：长期以来,各行业都广泛使用异常本地化。先前的研究重点是,在不适应目标数据集的情况下,对正常特征的分布进行近似分配,而不对目标数据集进行调整。然而,由于异常本地化应准确地区分正常和异常特征,因此不适应可能会使异常特征的正常性被高估。因此,我们提议采用适应目标数据集的特征,实现复杂的异常本地化,基于双双双双的功能适应(CFA)实现复杂的异常本地化。CFA包括:(1) 学习和嵌入目标导向特征的可学习补丁描述符,和(2) 独立于目标数据集大小的可缩放记忆库。此外,AFAFA采用转移学习来增加正常特征密度,以便通过对预先培训的CNN应用补丁描述和记忆库来明确区分异常性特征。拟议方法在数量和质量上超越了先前的方法。例如,它提供了异常检测为99.5%的AUROC分数和MVTec AD异常本地化基准的98.5%分。此外,该文件还指明了事先培训的常规/CNNC标准对目标的偏差效应的消极影响效应。