DeSTSeg: Segmentation Guided Denoising Student-Teacher for Anomaly Detection

Visual anomaly detection, an important problem in computer vision, is usually formulated as a one-class classification and segmentation task. The student-teacher (S- T) framework has proved to be effective in solving this chal-lenge. However, previous works based on S-T only empirically applied constraints on normal data and fused multilevel information. In this study, we propose an improved model called DeS TSeg, which integrates a pre-trained teacher network, a denoising student encoder-decoder, and a segmentation network into one framework. First, to strengthen the constraints on anomalous data, we intro-duce a denoising procedure that allows the student net-work to learn more robust representations. From…