To Generalize or Not to Generalize: Towards Autoencoders in One-Class Classification

In One-Class Classification (OCC), data affiliated with only one given class are accessible during training, while the trained algorithm must be capable of distinguishing the given class from all other unknown classes during test. OCC can be considered as a general case for many anomaly detection and novelty discovery problems and is more challenging than conventional binary and multi-class classification due to the absence of unknown classes. In literature, one of the most popular techniques towards OCC is autoencoder because autoencoders can capture the intrinsic structure of training data and are expected to have smaller reconstruction errors for the given class than those of unknown classes. As a result, during test, data from the known and unknown classes can be discriminated by thresholds. Despite the wide application of autoencoders in OCC, we have recently discovered that autoencoders can easily generalize to unknown classes, although they are trained on one given class only. The unexpected behavior reduces the overall OCC performance and leads to performance degradation during training. This paper proposes a novel solution to regularize the generalization ability of autoencoders and reduce performance degradation in OCC by introducing a feature weighting block in the latent space of autoencoders. Intensive experiments show that our method enables a training without performance degradation and significantly improves the OCC ability in comparison with contemporary state-of-the-art approaches.