A Novel Contrastive Learning Framework for Self-Supervised Anomaly Detection

Anomaly detection is significant in the field of computer vision and refers to identifying those samples in dataset that are different from normal samples. In practice, abnormal products are rare and anomaly detection usually calculates the difference between the inputs and the reconstructed images by reconstruction-based methods. Contrastive learning both maximizes the similarity between a sample and its augmentations, and the differences between different samples, which is suitable for improving the detection capability of the autoencoder. Inspired by this, we design a novel contrastive learning architecture for anomaly detection. In this work, we make reasonable sample pairs to simulate possible real anomalies and maximizes the distance between normal and abnormal samples. Remarkably, our approach improves the vanilla autoencoder model by 14.4% in terms of the AUROC score on the MVTec AD.