  The goal of continual learning (CL) is to efficiently update a machine
learning model with new data without forgetting previously-learned knowledge.
Most widely-used CL methods rely on a rehearsal memory of data points to be
reused while training on new data. Curating such a rehearsal memory to maintain
a small, informative subset of all the data seen so far is crucial to the
success of these methods. We devise a coreset selection method for
rehearsal-based continual learning. Our method is based on the idea of gradient
matching: The gradients induced by the coreset should match, as closely as
possible, those induced by the original training dataset. Inspired by the
neural tangent kernel theory, we perform this gradient matching across the
model's initialization distribution, allowing us to extract a coreset without
having to train the model first. We evaluate the method on a wide range of
continual learning scenarios and demonstrate that it improves the performance
of rehearsal-based CL methods compared to competing memory management
strategies such as reservoir sampling.
