A Reconstruction-Based Feature Adaptation for Anomaly Detection with Self-Supervised Multi-Scale Aggregation

Most previous embedding-based methods for anomaly detection directly utilize the visual features extracted from pretrained CNN network. However, there usually exists a gap of domain between pretrained data and target data in anomaly detection. To alleviate this discrepancy, we introduce ReconFA in this paper, a self-supervised domain adaptation method for anomaly detection: firstly, we design a self-supervised multi-scale features fusion method with multi-scale aggregation module (MSAM) to enhance interaction of multi-level outputs of pretrained CNN. Secondly, we train an encoder to adapt the features extracted from pretrained CNN to target domain by a feature-reconstruction task. Meanwhile, we use encoder to reduce the dimension of features to save space complexity. Extensive experiments show that our ReconFA method outperforms previous methods on MVTec AD datasets and on more challenging and sophisticated datasets MPDD.