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.

翻译：类增量学习（CIL）是持续学习中一个具有挑战性的场景，要求按顺序学习一系列任务，每个任务包含一组独特的类别。CIL的关键特点在于测试时不会提供任务标识符（task-id）。为每个测试样本预测任务标识是一个难题。一种新兴的理论指导方法（称为TIL+OOD）基于任务增量学习（TIL）方法，为每个任务在共享网络中训练一个特定任务模型，以应对灾难性遗忘，其中每个任务的模型是分布外（OOD）检测器而非传统分类器。该OOD检测器既能进行任务内（分布内（IND））类别预测，也能进行OOD检测。OOD检测能力是推理过程中预测任务标识的关键。然而，本文认为，使用传统OOD检测器进行任务标识预测是次优的，因为CIL中可用的额外信息（例如重放数据和已学习任务）可以被利用来设计更优且原理性的任务标识预测方法。我们称新方法为TPL（基于似然比的任务标识预测）。TPL显著优于强大的CIL基线，且几乎无灾难性遗忘。TPL的代码已公开于https://github.com/linhaowei1/TPL。