  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.
