Lee, S; Mannelli, SS; Clopath, C; Goldt, S; Saxe, A; (2022) Maslow's Hammer for Catastrophic Forgetting: Node Re-Use vs Node Activation. In: Proceedings of the 39th International Conference on Machine Learning. (pp. pp. 12455-12477). PMLR

Preview

Text lee22g.pdf - Published Version Download (19MB) | Preview

Abstract

Continual learning-learning new tasks in sequence while maintaining performance on old tasks-remains particularly challenging for artificial neural networks. Surprisingly, the amount of forgetting does not increase with the dissimilarity between the learned tasks, but appears to be worst in an intermediate similarity regime. In this paper we theoretically analyse both a synthetic teacher-student framework and a real data setup to provide an explanation of this phenomenon that we name Maslow's hammer hypothesis. Our analysis reveals the presence of a trade-off between node activation and node re-use that results in worst forgetting in the intermediate regime. Using this understanding we reinterpret popular algorithmic interventions for catastrophic interference in terms of this trade-off, and identify the regimes in which they are most effective.

Download activity - last month

Export as XMLJSONCSV

Download activity - last 12 months

Export as XMLJSONCSV

Downloads by country - last 12 months

Export as XMLJSONCSV

Archive Staff Only

View Item