Continual Learning Through One-Class Classification Using VAE

Abstract

Artificial neural networks (ANNs) suffer from catastrophic forgetting, a sharp decrease in performance on previously learned tasks, when trained on a new task without constant rehearsal. In this paper, we propose a new method for overcoming this phenomenon based on one-class classification. It is not only able to incrementally learn new but also detect unknown classes. This is a desirable property, since it enables the detection of new and unknown classes in a stream of data and adaption to a changing environment. Experiments on commonly used continual learning setups show competitive results and verify the concept.