  According to Complementary Learning Systems (CLS)
theory~\citep{mcclelland1995there} in neuroscience, humans do effective
\emph{continual learning} through two complementary systems: a fast learning
system centered on the hippocampus for rapid learning of the specifics and
individual experiences, and a slow learning system located in the neocortex for
the gradual acquisition of structured knowledge about the environment.
Motivated by this theory, we propose a novel continual learning framework named
"DualNet", which comprises a fast learning system for supervised learning of
pattern-separated representation from specific tasks and a slow learning system
for unsupervised representation learning of task-agnostic general
representation via a Self-Supervised Learning (SSL) technique. The two fast and
slow learning systems are complementary and work seamlessly in a holistic
continual learning framework. Our extensive experiments on two challenging
continual learning benchmarks of CORE50 and miniImageNet show that DualNet
outperforms state-of-the-art continual learning methods by a large margin. We
further conduct ablation studies of different SSL objectives to validate
DualNet's efficacy, robustness, and scalability. Code will be made available
upon acceptance.
