Sequential Learning in Feedforward Networks: Proactive and Retroactive Interference Minimization

摘要

We tackle the catastrophic interference problem with a formal approach. The problem is divided into two subproblems. The first arises when one tries to introduce some new information in a previously trained network, without distorting the stored information. The second is how to encode a set of patterns so as to preserve them when new information has to be stored. We suggest solutions to both subproblems without using local representations or retraining.