  We devise a coreset selection method 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. We evaluate the method in the
context of continual learning, where it can be used to curate a rehearsal
memory. Our method performs strong competitors such as reservoir sampling
across a range of memory sizes.
