  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.
