  Catastrophic interference has been a major roadblock in the research of
continual learning. Here we propose a variant of the back-propagation
algorithm, "conceptor-aided back-prop" (CAB), in which gradients are shielded
by conceptors against degradation of previously learned tasks. Conceptors have
their origin in reservoir computing, where they have been previously shown to
overcome catastrophic forgetting. CAB extends these results to deep feedforward
networks. On the disjoint MNIST task CAB outperforms two other methods for
coping with catastrophic interference that have recently been proposed in the
deep learning field.
