  Recent studies on catastrophic forgetting during sequential learning
typically focus on fixing the accuracy of the predictions for a previously
learned task. In this paper we argue that the outputs of neural networks are
subject to rapid changes when learning a new data distribution, and networks
that appear to "forget" everything still contain useful representation towards
previous tasks. Instead of enforcing the output accuracy to stay the same, we
propose to reduce the effect of catastrophic forgetting on the representation
level, as the output layer can be quickly recovered later with a small number
of examples. Towards this goal, we propose an experimental setup that measures
the amount of representational forgetting, and develop a novel meta-learning
algorithm to overcome this issue. The proposed meta-learner produces weight
updates of a sequential learning network, mimicking a multi-task teacher
network's representation. We show that our meta-learner can improve its learned
representations on new tasks, while maintaining a good representation for old
tasks.
