Evaluating Influence Functions for Memory Replay in Continual Learning

Replaying examples from previous tasks is a popular way to overcome catastrophic forgetting in machine learning systems aimed at continual learning (CL). Effectively selecting these examples to honor memory and time constraints however, is a challenging problem. We exeprimented several prin-cipled approaches for fixed-memory replay sampling derived from ‘Influence’ functions (Cook & Weisberg, 1982) to select useful examples that could help overcome catastrophic forgetting on previously encountered tasks. We performed an in-depth study of the effectiveness of influence-based sampling on the Split-MNIST benchmark dataset in three different continual learning settings and compare with other competitive subset sampling techniques.
We empirically evaluated Herding, K-means and Influence function-based sampling as effective replay sampling strategies across different CL settings.