Vertical-horizontal latent space with iterative memory review network for multi-class anomaly detection

Abstract

Anomaly detection has been recently proposed as a visual scene understanding task and widely applied in industrial detection. Traditional unsupervised methods aim to enhance anomaly detection performance by improving input reconstruction quality. However, these approaches rarely address the problem of poor quality reconstruction information. The inferior information affects the reconstruction effect of the input images, and further weakens the recognition performance of anomaly detection. Moreover, existing methods ignore the features of generated images and information interaction between modules, resulting in insufficient use of information. In this paper, a novel Iterative Memory Review Network (IMRN) based on horizontal-vertical latent space is presented to detect anomalies by selecting prior reconstruction information and strengthening the interactivity between network modules. An iterative memory module is recommended for the generator to encode high-dimensional inputs. Meanwhile, a review module is proposed for image modeling by extracting local and global features and fusing reconstruction process information. While completing the expansion of horizontal latent space, the review module also encodes vertical information in different directions from the iterative memory module. Our proposed approach is validated in multi-class anomaly detection tasks on public datasets. Extensive experimental results on the MVTec dataset verify the superiority of our proposed IMRN.