Unsupervised Generative Variational Continual Learning

Continual learning aims at learning a sequence of tasks without forgetting any task. While most of the existing literature in continual learning is aimed at class incremental learning in a supervised setting, there is an enormous potential for unsupervised continual learning using generative models. This paper proposes a combination of architectural pruning and neuron addition in generative variational models toward unsupervised generative continual learning (UGCL). Evaluations on standard benchmark data sets demonstrate the superior generative ability of the proposed method.