  Class incremental learning (CIL) is a challenging setting of continual
learning, which learns a series of tasks sequentially. Each task consists of a
set of unique classes. The key feature of CIL is that no task identifier (or
task-id) is provided at test time. Predicting the task-id for each test sample
is a challenging problem. An emerging theory-guided approach (called TIL+OOD)
is to train a task-specific model for each task in a shared network for all
tasks based on a task-incremental learning (TIL) method to deal with
catastrophic forgetting. The model for each task is an out-of-distribution
(OOD) detector rather than a conventional classifier. The OOD detector can
perform both within-task (in-distribution (IND)) class prediction and OOD
detection. The OOD detection capability is the key to task-id prediction during
inference. However, this paper argues that using a traditional OOD detector for
task-id prediction is sub-optimal because additional information (e.g., the
replay data and the learned tasks) available in CIL can be exploited to design
a better and principled method for task-id prediction. We call the new method
TPL (Task-id Prediction based on Likelihood Ratio). TPL markedly outperforms
strong CIL baselines and has negligible catastrophic forgetting. The code of
TPL is publicly available at https://github.com/linhaowei1/TPL.
