Learning and generalisation in a stable network

摘要

Most neural networks suffer from excessive plasticity, the learning of new information interferes with information already stored in the network, In this paper we review the pseudorehearsal solution to this problem proposed by Robins (1995). By localising the changes to the function learned by the network pseudorehearsal allows networks to be stable in the face of new learning, successfully integrating both new and previously learned information. In this paper we explore the impact that this mechanism has on the ability of the network to generalise.