  Existing continual learning techniques focus on either task incremental
learning (TIL) or class incremental learning (CIL) problem, but not both. CIL
and TIL differ mainly in that the task-id is provided for each test sample
during testing for TIL, but not provided for CIL. Continual learning methods
intended for one problem have limitations on the other problem. This paper
proposes a novel unified approach based on out-of-distribution (OOD) detection
and task masking, called CLOM, to solve both problems. The key novelty is that
each task is trained as an OOD detection model rather than a traditional
supervised learning model, and a task mask is trained to protect each task to
prevent forgetting. Our evaluation shows that CLOM outperforms existing
state-of-the-art baselines by large margins. The average TIL/CIL accuracy of
CLOM over six experiments is 87.6/67.9% while that of the best baselines is
only 82.4/55.0%.
