  Continual learning aims to learn a sequence of tasks from dynamic data
distributions. Without accessing to the old training samples, knowledge
transfer from the old tasks to each new task is difficult to determine, which
might be either positive or negative. If the old knowledge interferes with the
learning of a new task, i.e., the forward knowledge transfer is negative, then
precisely remembering the old tasks will further aggravate the interference,
thus decreasing the performance of continual learning. By contrast, biological
neural networks can actively forget the old knowledge that conflicts with the
learning of a new experience, through regulating the learning-triggered
synaptic expansion and synaptic convergence. Inspired by the biological active
forgetting, we propose to actively forget the old knowledge that limits the
learning of new tasks to benefit continual learning. Under the framework of
Bayesian continual learning, we develop a novel approach named Active
Forgetting with synaptic Expansion-Convergence (AFEC). Our method dynamically
expands parameters to learn each new task and then selectively combines them,
which is formally consistent with the underlying mechanism of biological active
forgetting. We extensively evaluate AFEC on a variety of continual learning
benchmarks, including CIFAR-10 regression tasks, visual classification tasks
and Atari reinforcement tasks, where AFEC effectively improves the learning of
new tasks and achieves the state-of-the-art performance in a plug-and-play way.
