  Continual learning in environments with shifting data distributions is a
challenging problem with several real-world applications. In this paper we
consider settings in which the data distribution(task) shifts abruptly and the
timing of these shifts are not known. Furthermore, we consider a
semi-supervised task-agnostic setting in which the learning algorithm has
access to both task-segmented and unsegmented data for offline training. We
propose a novel approach called mixture of Basismodels (MoB) for addressing
this problem setting. The core idea is to learn a small set of basis models and
to construct a dynamic, task-dependent mixture of the models to predict for the
current task. We also propose a new methodology to detect observations that are
out-of-distribution with respect to the existing basis models and to
instantiate new models as needed. We test our approach in multiple domains and
show that it attains better prediction error than existing methods in most
cases while using fewer models than other multiple model approaches. Moreover,
we analyze the latent task representations learned by MoB and show that similar
tasks tend to cluster in the latent space and that the latent representation
shifts at the task boundaries when tasks are dissimilar.
