Contrastive Learning Approach for Anomaly Detection in Multi-View Scenarios

Quality control is a key task in smart manufacturing, since it ensures that processes consistently meet rigorous performance standards. The effective implementation of these mechanisms is crucial to ensuring both reliability and efficiency in modern manufacturing environments, where automation is increasingly integrated. Traditional anomaly detection algorithms typically rely on single-view data for each manufacturing product, overlooking relevant and complementary information available from multiple perspectives. Furthermore, cross-entropy-based loss functions are frequently adopted in the literature to train detection models; however, these approaches often struggle with imbalanced datasets or when detecting rare and subtle anomalies. In this work, a contrastive learning architecture for multi-view anomaly detection in industrial settings is proposed. The method performs a mid-level fusion to generate a structured representation of the input instances, thereby enhancing detection capabilities. The architecture was evaluated on the Real-IAD dataset, where it demonstrated better performance than traditional techniques. These findings highlight the potential of contrastive learning to improve anomaly detection performance, thus contributing to the construction of more reliable quality control systems in smart manufacturing environments.