Anomaly Detection Based on Selection and Weighting in Latent Space
This work addresses anomaly detection for industrial safety and reliability, but it is incremental as it builds on existing autoencoder techniques.
The paper tackles the problem of anomaly detection in industrial settings by proposing a selection-and-weighting framework (SWAD) that improves autoencoder performance by focusing on latent representations, achieving comparable or better results than state-of-the-art methods on benchmark datasets.
With the high requirements of automation in the era of Industry 4.0, anomaly detection plays an increasingly important role in higher safety and reliability in the production and manufacturing industry. Recently, autoencoders have been widely used as a backend algorithm for anomaly detection. Different techniques have been developed to improve the anomaly detection performance of autoencoders. Nonetheless, little attention has been paid to the latent representations learned by autoencoders. In this paper, we propose a novel selection-and-weighting-based anomaly detection framework called SWAD. In particular, the learned latent representations are individually selected and weighted. Experiments on both benchmark and real-world datasets have shown the effectiveness and superiority of SWAD. On the benchmark datasets, the SWAD framework has reached comparable or even better performance than the state-of-the-art approaches.