Efficient Meta-Learning for Continual Learning with Taylor Expansion Approximation
This addresses the problem of catastrophic forgetting for AI systems handling sequential tasks, but it is incremental as it builds on existing meta-learning approaches.
The paper tackles catastrophic forgetting in continual learning by proposing an efficient meta-learning algorithm that uses Taylor expansion approximation to adapt regularization and learning rates, achieving better or on-par performance with higher efficiency on diverse benchmarks.
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.