Online Continual Learning on Class Incremental Blurry Task Configuration with Anytime Inference

Understand

Despite rapid advances in continual learning, a large body of research is devoted to improving performance in the existing setups.

• While a handful of work do propose new continual learning setups, they still lack practicality in certain aspects.
• For better practicality, we first propose a novel continual learning setup that is online, task-free, class-incremental, of blurry task boundaries and subject to inference queries at any moment.
• We additionally propose a new metric to better measure the performance of the continual learning methods subject to inference queries at any moment.