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Continual learning aims to enable machine learning models to learn a generalsolution space for past and future tasks in a sequential manner. Conventionalmodels tend to forget the knowledge of previous tasks while learning a newtask, a phenomenon known as catastrophic forgetting. When using Bayesian modelsin continual learning, knowledge from previous tasks can be retained in twoways: 1). posterior distributions over the parameters, containing the knowledgegained from inference in previous tasks, which then serve as the priors for thefollowing task; 2). coresets, containing knowledge of data distributions ofprevious tasks. Here, we show that Bayesian continual learning can befacilitated in terms of these two means through the use of natural gradientsand Stein gradients respectively.

Facilitating Bayesian Continual Learning by Natural Gradients and Stein Gradients