  Though neural networks have achieved much progress in various applications,
it is still highly challenging for them to learn from a continuous stream of
tasks without forgetting. Continual learning, a new learning paradigm, aims to
solve this issue. In this work, we propose a new model for continual learning,
called Bayesian Optimized Continual Learning with Attention Mechanism (BOCL)
that dynamically expands the network capacity upon the arrival of new tasks by
Bayesian optimization and selectively utilizes previous knowledge (e.g. feature
maps of previous tasks) via attention mechanism. Our experiments on variants of
MNIST and CIFAR-100 demonstrate that our methods outperform the
state-of-the-art in preventing catastrophic forgetting and fitting new tasks
better.
