  Continual learning with neural networks is an important learning framework in
AI that aims to learn a sequence of tasks well. However, it is often confronted
with three challenges: (1) overcome the catastrophic forgetting problem, (2)
adapt the current network to new tasks, and meanwhile (3) control its model
complexity. To reach these goals, we propose a novel approach named as
Continual Learning with Efficient Architecture Search, or CLEAS in short. CLEAS
works closely with neural architecture search (NAS) which leverages
reinforcement learning techniques to search for the best neural architecture
that fits a new task. In particular, we design a neuron-level NAS controller
that decides which old neurons from previous tasks should be reused (knowledge
transfer), and which new neurons should be added (to learn new knowledge). Such
a fine-grained controller allows one to find a very concise architecture that
can fit each new task well. Meanwhile, since we do not alter the weights of the
reused neurons, we perfectly memorize the knowledge learned from previous
tasks. We evaluate CLEAS on numerous sequential classification tasks, and the
results demonstrate that CLEAS outperforms other state-of-the-art alternative
methods, achieving higher classification accuracy while using simpler neural
architectures.
