  In continual learning, new categories may be introduced over time, and an
ideal learning system should perform well on both the original categories and
the new categories. While deep neural nets have achieved resounding success in
the classical supervised setting, they are known to forget about knowledge
acquired in prior episodes of learning if the examples encountered in the
current episode of learning are drastically different from those encountered in
prior episodes. In this paper, we propose a new method that can both leverage
the expressive power of deep neural nets and is resilient to forgetting when
new categories are introduced. We found the proposed method can reduce
forgetting by 2.3x to 6.9x on CIFAR-10 compared to existing methods and by 1.8x
to 2.7x on ImageNet compared to an oracle baseline.
