Replay-free Online Continual Learning with Self-Supervised MultiPatches
This work addresses the problem of online continual learning for applications with strict privacy regulations where replay is forbidden.
The authors tackled the problem of online continual learning without replay, achieving better results than replay-based strategies. Their proposed method, Continual MultiPatches, surpasses existing self-supervised learning strategies on OCL streams.
Online Continual Learning (OCL) methods train a model on a non-stationary data stream where only a few examples are available at a time, often leveraging replay strategies. However, usage of replay is sometimes forbidden, especially in applications with strict privacy regulations. Therefore, we propose Continual MultiPatches (CMP), an effective plug-in for existing OCL self-supervised learning strategies that avoids the use of replay samples. CMP generates multiple patches from a single example and projects them into a shared feature space, where patches coming from the same example are pushed together without collapsing into a single point. CMP surpasses replay and other SSL-based strategies on OCL streams, challenging the role of replay as a go-to solution for self-supervised OCL.