  Approaches to continual learning aim to successfully learn a set of related
tasks that arrive in an online manner. Recently, several frameworks have been
developed which enable deep learning to be deployed in this learning scenario.
A key modelling decision is to what extent the architecture should be shared
across tasks. On the one hand, separately modelling each task avoids
catastrophic forgetting but it does not support transfer learning and leads to
large models. On the other hand, rigidly specifying a shared component and a
task-specific part enables task transfer and limits the model size, but it is
vulnerable to catastrophic forgetting and restricts the form of task-transfer
that can occur. Ideally, the network should adaptively identify which parts of
the network to share in a data driven way. Here we introduce such an approach
called Continual Learning with Adaptive Weights (CLAW), which is based on
probabilistic modelling and variational inference. Experiments show that CLAW
achieves state-of-the-art performance on six benchmarks in terms of overall
continual learning performance, as measured by classification accuracy, and in
terms of addressing catastrophic forgetting.
