  Human intelligence is characterized not only by the capacity to learn complex
skills, but the ability to rapidly adapt and acquire new skills within an
ever-changing environment. In this work we study how the learning of modular
solutions can allow for effective generalization to both unseen and potentially
differently distributed data. Our main postulate is that the combination of
task segmentation, modular learning and memory-based ensembling can give rise
to generalization on an exponentially growing number of unseen tasks. We
provide a concrete instantiation of this idea using a combination of: (1) the
Forget-Me-Not Process, for task segmentation and memory based ensembling; and
(2) Gated Linear Networks, which in contrast to contemporary deep learning
techniques use a modular and local learning mechanism. We demonstrate that this
system exhibits a number of desirable continual learning properties: robustness
to catastrophic forgetting, no negative transfer and increasing levels of
positive transfer as more tasks are seen. We show competitive performance
against both offline and online methods on standard continual learning
benchmarks.
