Reviews: Online Continual Learning with Maximal Interfered Retrieval

This paper describes an approach to improve rehearsal-based continual learning techniques (either replay-based or with a generative model) by identifying samples that are most useful to avoid forgetting. This is achieved by computing the increase in loss on the replayed samples, and using this to determine which samples should be used during learning. It is a simple and intuitive idea, the paper is clearly written, and experiments on multiple datasets are compelling. I think it could make a nice addition to the conference, but needs a few improvements first. My main criticism is that the approach requires a separate virtual gradient step for each actual step, to compute the change in loss on the replay samples.