Papers › Replay-free Online Continual Learning with Self-Supervised MultiPatches
Replay-free Online Continual Learning with Self-Supervised MultiPatches
Giacomo Cignoni, Andrea Cossu, Alex Gomez-Villa, Joost Van de Weijer, Antonio Carta
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.
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