Replay can provably increase forgetting
Keywords: Continual Learning, Replay, Linear Regression, Theory of Continual Learning
TL;DR: We study sample replay in continual linear regression and find that surprisingly sample replay can increase forgetting.
Abstract: Continual learning seeks to enable machine learning systems to solve an increasing corpus of tasks sequentially. A critical challenge for continual learning is forgetting, where the performance on previously learned tasks decreases as new tasks are introduced. One of the commonly used techniques to mitigate forgetting, sample replay, has been shown empirically to reduce forgetting by retaining some examples from old tasks and including them in new training episodes.
In this work, we provide a theoretical analysis of sample replay in an over-parameterized continual linear regression setting, where given enough replay samples, one would be able to eliminate forgetting. Our analysis focuses on replaying a few examples
and highlights the role of the replay samples and task subspaces.
Surprisingly, we find that forgetting can be non-monotonic with respect to the number of replay samples.
We construct tasks where replay of a single example can increase forgetting and even distributions where replay of a randomly selected sample increases forgetting on average. We provide empirical evidence that this is a property of the tasks rather than the model used to train on them, by showing a similar behavior for a neural net equipped with SGD.
Through experiments on a commonly used benchmark, we provide additional evidence that performance of the replay heavily depends on the choice of replay samples and the relationship between tasks.
Supplementary Material: zip
Primary Area: transfer learning, meta learning, and lifelong learning
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Submission Number: 12633
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