Information-theoretic Online Memory Selection for Continual Learning

Understand

A challenging problem in task-free continual learning is the online selection of a representative replay memory from data streams.

• In this work, we investigate the online memory selection problem from an information-theoretic perspective.
• To gather the most information, we propose the \textit{surprise} and the \textit{learnability} criteria to pick informative points and to avoid outliers.
• We present a Bayesian model to compute the criteria efficiently by exploiting rank-one matrix structures.