Memory association guided unsupervised anomaly detection with adaptive 3D attention

Unsupervised anomaly detection has recently made significant progress in various anomaly detection tasks, including multi-normal class anomaly detection and industrial defect detection. However, existing methods often construct simple feature spaces that struggle to disentangle the abundant anomalous information interwoven with the reconstruction information. To ensure the normalcy of the image feature space, we propose a Memory Association Module-based generator to activate deep interactive memory feature spaces, thereby enhancing the representation of normal feature information. Furthermore, we construct a feature simulation network that utilizes deep feature progressive fusion blocks to capture multi-scale information from the reconstructed image and subsequently corrects the vectors outputted by the memory feature space. Considering the challenges faced by existing methods in identifying edge information and blurry regions within defective images, we propose an adaptive 3D attention module and integrate it into the overall anomaly detection network architecture to enhance the network’s ability to identify hard-to-detect defective areas in images.