Continual Learning, Fast and Slow

• URL: http://arxiv.org/abs/2209.02370v3
• Date: Sun, 9 Jul 2023 10:02:41 GMT
• Title: Continual Learning, Fast and Slow
• Authors: Quang Pham, Chenghao Liu, Steven C. H. Hoi
• Abstract summary: According to the Complementary Learning Systems theory, humans do effective emphcontinual learning through two complementary systems. We propose emphDualNets (for Dual Networks), a general continual learning framework comprising a fast learning system for supervised learning of specific tasks and a slow learning system for representation learning of task-agnostic general representation via Self-Supervised Learning (SSL) We demonstrate the promising results of DualNets on a wide range of continual learning protocols, ranging from the standard offline, task-aware setting to the challenging online, task-free scenario.
• Score: 75.53144246169346
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: According to the Complementary Learning Systems (CLS) theory~\cite{mcclelland1995there} in neuroscience, humans do effective \emph{continual learning} through two complementary systems: a fast learning system centered on the hippocampus for rapid learning of the specifics, individual experiences; and a slow learning system located in the neocortex for the gradual acquisition of structured knowledge about the environment. Motivated by this theory, we propose \emph{DualNets} (for Dual Networks), a general continual learning framework comprising a fast learning system for supervised learning of pattern-separated representation from specific tasks and a slow learning system for representation learning of task-agnostic general representation via Self-Supervised Learning (SSL). DualNets can seamlessly incorporate both representation types into a holistic framework to facilitate better continual learning in deep neural networks. Via extensive experiments, we demonstrate the promising results of DualNets on a wide range of continual learning protocols, ranging from the standard offline, task-aware setting to the challenging online, task-free scenario. Notably, on the CTrL~\cite{veniat2020efficient} benchmark that has unrelated tasks with vastly different visual images, DualNets can achieve competitive performance with existing state-of-the-art dynamic architecture strategies~\cite{ostapenko2021continual}. Furthermore, we conduct comprehensive ablation studies to validate DualNets efficacy, robustness, and scalability. Code will be made available at \url{https://github.com/phquang/DualNet}.

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