BNS: Building Network Structures Dynamically for Continual Learning

Qi Qin, Han Peng, Wenpeng Hu, Dongyan Zhao, Bing Liu

Abstract:

Continual learning (CL) of a sequence of tasks is often accompanied with the
catastrophic forgetting (CF) problem. Existing research has achieved remarkable
results in overcoming CF, especially for task continual learning. However, limited
work has been done to achieve another important goal of CL, knowledge transfer.
In this paper, we propose a technique (called BNS) to do both. The novelty of
BNS is that it dynamically builds a network to learn each new task to overcome
CF and to transfer knowledge across tasks at the same time. Experimental results
show that when the tasks are different (with little shared knowledge), BNS can
already outperform the state-of-the-art baselines. When the tasks are similar and
have shared knowledge, BNS outperforms the baselines substantially by a large
margin due to its knowledge transfer capability