Efficient Meta-Learning for Continual Learning with Taylor Expansion Approximation

TL;DR

This paper introduces an efficient meta-learning algorithm for continual learning that uses Taylor expansion to adapt regularization and learning rates, effectively reducing catastrophic forgetting while maintaining high computational efficiency.

Contribution

It proposes a novel meta-learning method leveraging Taylor expansion for importance estimation, avoiding second-order derivatives, and employing Proximal Gradient Descent for improved efficiency.

Findings

Outperforms state-of-the-art methods on multiple benchmarks.

Achieves comparable or better accuracy with higher computational efficiency.

Effectively mitigates catastrophic forgetting in continual learning scenarios.

Abstract

Continual learning aims to alleviate catastrophic forgetting when handling consecutive tasks under non-stationary distributions. Gradient-based meta-learning algorithms have shown the capability to implicitly solve the transfer-interference trade-off problem between different examples. However, they still suffer from the catastrophic forgetting problem in the setting of continual learning, since the past data of previous tasks are no longer available. In this work, we propose a novel efficient meta-learning algorithm for solving the online continual learning problem, where the regularization terms and learning rates are adapted to the Taylor approximation of the parameter's importance to mitigate forgetting. The proposed method expresses the gradient of the meta-loss in closed-form and thus avoid computing second-order derivative which is computationally inhibitable. We also use Proximal Gradient Descent to further improve computational efficiency and accuracy. Experiments on diverse benchmarks show that our method achieves better or on-par performance and much higher efficiency compared to the state-of-the-art approaches.