  Continual learning aims to learn a sequence of tasks by leveraging the
knowledge acquired in the past in an online-learning manner while being able to
perform well on all previous tasks, this ability is crucial to the artificial
intelligence (AI) system, hence continual learning is more suitable for most
real-word and complex applicative scenarios compared to the traditional
learning pattern. However, the current models usually learn a generic
representation base on the class label on each task and an effective strategy
is selected to avoid catastrophic forgetting. We postulate that selecting the
related and useful parts only from the knowledge obtained to perform each task
is more effective than utilizing the whole knowledge. Based on this fact, in
this paper we propose a new framework, named Selecting Related Knowledge for
Online Continual Learning (SRKOCL), which incorporates an additional efficient
channel attention mechanism to pick the particular related knowledge for every
task. Our model also combines experience replay and knowledge distillation to
circumvent the catastrophic forgetting. Finally, extensive experiments are
conducted on different benchmarks and the competitive experimental results
demonstrate that our proposed SRKOCL is a promised approach against the
state-of-the-art.
