  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.
