The Ideal Continual Learner: An Agent That Never Forgets

By Liangzu Peng, Paris V. Giampouras, René Vidal. 2023

44 citations recorded by the source corpus.

The goal of continual learning is to find a model that solves multiple learning tasks which are presented sequentially to the learner. A key challenge in this setting is that the learner may forget how to solve a previous task when learning a new task, a phenomenon known as catastrophic forgetting. To address this challenge, many practical methods have been proposed, including memory-based, regularization-based, and expansion-based methods. However, a rigorous theoretical understanding of these methods remains elusive. This paper aims to bridge this gap between theory and practice by proposing a new continual learning framework called Ideal Continual Learner (ICL), which is guaranteed to avoid catastrophic forgetting by construction. We show that ICL unifies multiple well-established continual learning methods and gives new theoretical insights into the strengths and weaknesses of these methods. We also derive generalization bounds for ICL which allow us to theoretically quantify how rehearsal affects generalization. Finally, we connect ICL to several classic subjects and research topics of modern interest, which allows us to make historical remarks and inspire future directions.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Meta & Continual Learning

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Citations in the corpus

A Comprehensive Survey of Continual Learning: Theory, Method and Application (2023, neutral): “For example, with probability at least 1 − δ for any δ ∈ (0 , 1) , an ideal continual learner [102] under the assumption that all tasks share a global minimizer with uniform convergence (i.e., λ max i = λ for ∀ t = 1 , . . . , k in (13)) has the generalization bound where c ∗ t = (cid:4) t ( θ ∗ t…”
Continual Learning in Linear Classification on Separable Data (2023, background): “We present a novel result showing that in continual regression, such weighting schemes prevent forgetting (thus forming an “ideal continual learner” as defined by Peng et al. (2023)).”