论文标题
通过将多尺度记忆应用于自动编码器来改善无监督的异常本质
Improving unsupervised anomaly localization by applying multi-scale memories to autoencoders
论文作者
论文摘要
Autoencoder and its variants have been widely applicated in anomaly detection.The previous work memory-augmented deep autoencoder proposed memorizing normality to detect anomaly, however it neglects the feature discrepancy between different resolution scales, therefore we introduce multi-scale memories to record scale-specific features and multi-scale attention fuser between the encoding and decoding module of the autoencoder for异常检测，即MMAE.MMAE在相应的分辨率量表上更新插槽，作为无监督学习过程中的原型特征。对于异常检测，我们通过用最相关的原型特征替换每个刻度的原始编码图像特征来完成异常删除，并在进送到解码模块以重建图像之前融合这些功能。各种数据集的实验结果证明，与基于重建的方法相比，我们的MMAE在不同尺度上成功地消除了异常，并且在几个数据集上表现出色。
Autoencoder and its variants have been widely applicated in anomaly detection.The previous work memory-augmented deep autoencoder proposed memorizing normality to detect anomaly, however it neglects the feature discrepancy between different resolution scales, therefore we introduce multi-scale memories to record scale-specific features and multi-scale attention fuser between the encoding and decoding module of the autoencoder for anomaly detection, namely MMAE.MMAE updates slots at corresponding resolution scale as prototype features during unsupervised learning. For anomaly detection, we accomplish anomaly removal by replacing the original encoded image features at each scale with most relevant prototype features,and fuse these features before feeding to the decoding module to reconstruct image. Experimental results on various datasets testify that our MMAE successfully removes anomalies at different scales and performs favorably on several datasets compared to similar reconstruction-based methods.
