  Rehearsal, retraining on a stored small data subset of old tasks, has been
proven effective in solving catastrophic forgetting in continual learning.
However, due to the sampled data may have a large bias towards the original
dataset, retraining them is susceptible to driving continual domain drift of
old tasks in feature space, resulting in forgetting. In this paper, we focus on
tackling the continual domain drift problem with centroid distance
distillation. First, we propose a centroid caching mechanism for sampling data
points based on constructed centroids to reduce the sample bias in rehearsal.
Then, we present a centroid distance distillation that only stores the centroid
distance to reduce the continual domain drift. The experiments on four
continual learning datasets show the superiority of the proposed method, and
the continual domain drift can be reduced.
