Self-Attention Memory-Augmented Wavelet-CNN for Anomaly Detection

Anomaly detection plays an important role in manufacturing quality control/assurance. Among approaches adopting computer vision techniques, reconstruction-based methods learn a content-aware mapping function that transfers abnormal regions to normal regions in an unsupervised manner. Such methods usually have difficulty in improving both the reconstruction quality and capacity for abnormal discovery. We observe that high-level semantic contextual features demonstrate a strong ability for abnormal discovery, while variational features help to preserve fine image details. Inspired by the observation, we propose a new abnormal detection model by utilizing features for different purposes depending on their frequency characteristics. The 2D-discrete wavelet transform (DWT) is introduced to obtain the low-frequency and high-frequency components of features and further used to generate the two essential features following different routing paths in our encoder process. To further improve the capacity for abnormal discovery, we propose a novel feature augmentation module that is informed by a customized self-attention mechanism. Extensive experiments are conducted on two popular datasets: MVTec AD and BTAD. The experimental results illustrate that the proposed method outperforms other state-of-the-art approaches in terms of the image-level AUROC score. In particular, our method achieves 100% of the image-level AUROC score on 8 out of 15 classes on the MVTec dataset.