Online Continual Learning via the Meta-learning Update with Multi-scale Knowledge Distillation and Data Augmentation

TL;DR

This paper introduces MMKDDA, a novel meta-learning framework for online continual learning that employs multi-scale knowledge distillation and data augmentation to mitigate catastrophic forgetting and balance stability and plasticity.

Contribution

The paper proposes a new online continual learning method combining multi-scale knowledge distillation, data augmentation, and meta-learning updates to address data imbalance and stability-plasticity dilemma.

Findings

Outperforms baseline methods on four benchmark datasets.

Effectively alleviates catastrophic forgetting.

Ablation studies confirm the importance of each component.

Abstract

Continual learning aims to rapidly and continually learn the current task from a sequence of tasks. Compared to other kinds of methods, the methods based on experience replay have shown great advantages to overcome catastrophic forgetting. One common limitation of this method is the data imbalance between the previous and current tasks, which would further aggravate forgetting. Moreover, how to effectively address the stability-plasticity dilemma in this setting is also an urgent problem to be solved. In this paper, we overcome these challenges by proposing a novel framework called Meta-learning update via Multi-scale Knowledge Distillation and Data Augmentation (MMKDDA). Specifically, we apply multiscale knowledge distillation to grasp the evolution of long-range and short-range spatial relationships at different feature levels to alleviate the problem of data imbalance. Besides, our method mixes the samples from the episodic memory and current task in the online continual training procedure, thus alleviating the side influence due to the change of probability distribution. Moreover, we optimize our model via the meta-learning update resorting to the number of tasks seen previously, which is helpful to keep a better balance between stability and plasticity. Finally, our experimental evaluation on four benchmark datasets shows the effectiveness of the proposed MMKDDA framework against other popular baselines, and ablation studies are also conducted to further analyze the role of each component in our framework.