  This paper argues that continual learning methods can benefit by splitting
the capacity of the learner across multiple models. We use statistical learning
theory and experimental analysis to show how multiple tasks can interact with
each other in a non-trivial fashion when a single model is trained on them. The
generalization error on a particular task can improve when it is trained with
synergistic tasks, but can also deteriorate when trained with competing tasks.
This theory motivates our method named Model Zoo which, inspired from the
boosting literature, grows an ensemble of small models, each of which is
trained during one episode of continual learning. We demonstrate that Model Zoo
obtains large gains in accuracy on a variety of continual learning benchmark
problems. Code is available at
https://github.com/grasp-lyrl/modelzoo_continual.
