  Continual Learning (CL) algorithms have recently received a lot of attention
as they attempt to overcome the need to train with an i.i.d. sample from some
unknown target data distribution. Building on prior work, we study principled
ways to tackle the CL problem by adopting a Bayesian perspective and focus on
continually learning a task-specific posterior distribution via a shared
meta-model, a task-conditioned hypernetwork. This approach, which we term
Posterior-replay CL, is in sharp contrast to most Bayesian CL approaches that
focus on the recursive update of a single posterior distribution. The benefits
of our approach are (1) an increased flexibility to model solutions in weight
space and therewith less susceptibility to task dissimilarity, (2) access to
principled task-specific predictive uncertainty estimates, that can be used to
infer task identity during test time and to detect task boundaries during
training, and (3) the ability to revisit and update task-specific posteriors in
a principled manner without requiring access to past data. The proposed
framework is versatile, which we demonstrate using simple posterior
approximations (such as Gaussians) as well as powerful, implicit distributions
modelled via a neural network. We illustrate the conceptual advance of our
framework on low-dimensional problems and show performance gains on computer
vision benchmarks.
