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Due to their inference, data representation and reconstruction properties,Variational Autoencoders (VAE) have been successfully used in continuallearning classification tasks. However, their ability to generate images withspecifications corresponding to the classes and databases learned duringContinual Learning (CL) is not well understood and catastrophic forgettingremains a significant challenge. In this paper, we firstly analyze theforgetting behaviour of VAEs by developing a new theoretical framework thatformulates CL as a dynamic optimal transport problem. This framework provesapproximate bounds to the data likelihood without requiring the taskinformation and explains how the prior knowledge is lost during the trainingprocess. We then propose a novel memory buffering approach, namely the OnlineCooperative Memorization (OCM) framework, which consists of a Short-Term Memory(STM) that continually stores recent samples to provide future information forthe model, and a Long-Term Memory (LTM) aiming to preserve a wide diversity ofsamples. The proposed OCM transfers certain samples from STM to LTM accordingto the information diversity selection criterion without requiring anysupervised signals. The OCM framework is then combined with a dynamic VAEexpansion mixture network for further enhancing its performance.

Continual Variational Autoencoder Learning via Online Cooperative Memorization