The Joint Effect of Task Similarity and Overparameterization on Catastrophic Forgetting — An Analytical Model

Published: 16 Jan 2024, Last Modified: 21 Apr 2024ICLR 2024 posterEveryoneRevisionsBibTeX
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Keywords: deep learning, continual learning, overparameterization, task similarity, catastrophic forgetting, theory
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TL;DR: Analyze the joint effect of task similarity and overparameterization on catastrophic forgetting by deriving an exact analytical expression for forgetting in a two-task random orthogonal transformation problem.
Abstract: In continual learning, catastrophic forgetting is affected by multiple aspects of the tasks. Previous works have analyzed separately how forgetting is affected by either task similarity or overparameterization. In contrast, our paper examines how task similarity and overparameterization jointly affect forgetting in an analyzable model. Specifically, we focus on two-task continual linear regression, where the second task is a random orthogonal transformation of an arbitrary first task (an abstraction of random permutation tasks). We derive an exact analytical expression for the expected forgetting — and uncover a nuanced pattern. In highly overparameterized models, intermediate task similarity causes the most forgetting. However, near the interpolation threshold, forgetting decreases monotonically with the expected task similarity. We validate our findings with linear regression on synthetic data, and with neural networks on established permutation task benchmarks.
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Primary Area: transfer learning, meta learning, and lifelong learning
Submission Number: 2816