Exploiting Autoencoder's Weakness to Generate Pseudo Anomalies
This addresses a specific weakness in anomaly detection methods for applications like surveillance and cybersecurity, but it is incremental as it builds on existing autoencoder frameworks.
The paper tackles the problem of autoencoders reconstructing anomalies too well in anomaly detection by generating pseudo anomalies from learned adaptive noise, which improves the discriminative capability of autoencoders across multiple datasets.
Due to the rare occurrence of anomalous events, a typical approach to anomaly detection is to train an autoencoder (AE) with normal data only so that it learns the patterns or representations of the normal training data. At test time, the trained AE is expected to well reconstruct normal but to poorly reconstruct anomalous data. However, contrary to the expectation, anomalous data is often well reconstructed as well. In order to further separate the reconstruction quality between normal and anomalous data, we propose creating pseudo anomalies from learned adaptive noise by exploiting the aforementioned weakness of AE, i.e., reconstructing anomalies too well. The generated noise is added to the normal data to create pseudo anomalies. Extensive experiments on Ped2, Avenue, ShanghaiTech, CIFAR-10, and KDDCUP datasets demonstrate the effectiveness and generic applicability of our approach in improving the discriminative capability of AEs for anomaly detection.