Continual learning (or class incremental learning) is a realistic learning scenario for computer vision systems, where deep neural networks are trained on episodic data, and the data from previous episodes are generally inaccessible to the model. Existing research in this domain has primarily focused on avoiding catastrophic forgetting, which occurs due to the continuously changing class distributions in each episode and the inaccessibility of the data from previous episodes. However, these methods assume that all the training samples in every episode are annotated; this not only incurs a huge annotation cost, but also results in a wastage of annotation effort, since most of the samples in a given episode will not be accessible to the model in subsequent episodes. Active learning algorithms identify the salient and informative samples from large amounts of unlabeled data and are instrumental in reducing the human annotation effort in inducing a deep neural network. In this paper, we propose ACIL, a novel active learning framework for class incremental learning settings. We exploit a criterion based on uncertainty and diversity to identify the exemplar samples that need to be annotated in each episode, and will be appended to the data in the next episode. Such a framework can drastically reduce annotation cost and can also avoid catastrophic forgetting. Our extensive empirical analyses on several vision datasets corroborate the promise and potential of our framework against relevant baselines.

翻译：持续学习（或称类增量学习）是计算机视觉系统中一种现实的学习场景，其中深度神经网络基于阶段性数据进行训练，且模型通常无法访问先前阶段的数据。该领域现有研究主要侧重于避免灾难性遗忘，这种现象源于每个阶段中不断变化的类别分布以及先前阶段数据的不可访问性。然而，这些方法默认每个阶段的所有训练样本均已标注；这不仅会产生巨大的标注成本，还会导致标注资源的浪费，因为给定阶段中的大多数样本在后续阶段将无法被模型访问。主动学习算法能够从大量未标注数据中识别出显著且信息丰富的样本，对于降低深度神经网络训练过程中的人工标注成本具有重要作用。本文提出ACIL——一种面向类增量学习场景的新型主动学习框架。我们基于不确定性与多样性的准则来识别每个阶段中需要标注的典型样本，这些样本将被追加至下一阶段的数据集中。该框架能够显著降低标注成本，同时避免灾难性遗忘。我们在多个视觉数据集上的广泛实证分析证实了本框架相较于相关基线方法的优越性与潜力。