Improving unsupervised anomaly localization by applying multi-scale memories to autoencoders
This work provides an incremental improvement for anomaly detection, which is a problem relevant to various industries for quality control and security.
The paper tackles the problem of unsupervised anomaly localization by introducing multi-scale memories to autoencoders. The proposed MMAE method successfully removes anomalies at different scales and performs favorably on several datasets compared to similar reconstruction-based methods.
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.