  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. To address the
challenging setup and evaluation protocol, we propose an effective method that
employs a new memory management scheme and novel learning techniques. Our
empirical validation demonstrates that the proposed method outperforms prior
arts by large margins. Code and data splits are available at
https://github.com/naver-ai/i-Blurry.
