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 Backprop" (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 and permuted MNIST tasks, CAB outperforms two other methods for coping with catastrophic interference that have recently been proposed.
We propose a variant of the backpropagation algorithm, in which gradients are shielded by conceptors against degradation of previously learned tasks. This paper applies the notion of conceptors, a form a regulariser, to prevent forgetting in continual learning in the training of neural networks on sequential tasks. Introduces a method for learning new tasks, without interfering previous tasks, using conceptors.