Bayesian Optimized Continual Learning with Attention Mechanism

Understand

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.