  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.
