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Autoencoder and its variants have been widely applicated in anomalydetection.The previous work memory-augmented deep autoencoder proposedmemorizing normality to detect anomaly, however it neglects the featurediscrepancy between different resolution scales, therefore we introducemulti-scale memories to record scale-specific features and multi-scaleattention fuser between the encoding and decoding module of the autoencoder foranomaly detection, namely MMAE.MMAE updates slots at corresponding resolutionscale as prototype features during unsupervised learning. For anomalydetection, we accomplish anomaly removal by replacing the original encodedimage features at each scale with most relevant prototype features,and fusethese features before feeding to the decoding module to reconstruct image.Experimental results on various datasets testify that our MMAE successfullyremoves anomalies at different scales and performs favorably on severaldatasets compared to similar reconstruction-based methods.

Improving unsupervised anomaly localization by applying multi-scale memories to autoencoders