在标注预算极低时，持续识别新类别并主动学习。
CUAL: Continual Uncertainty-aware Active Learner
- 根据不确定性筛选可疑样本，优先标注
- 同时对确定预测进行伪标签，提升学习效率
- 适用于部署后持续学习的新场景，适合研究者参考
实际应用中的AI需适应部署后的未知变化。本文针对一种挑战性且未充分研究的持续适应问题：部署后的AI持续接收未标注数据，其中可能包含已知类别的新样本或未知类别的新样本。在此设定下，仅能使用极小的标注预算来查询最具信息量的样本以实现持续学习。我们提出名为CUAL（Continual Uncertainty-aware Active Learner）的综合解决方案。CUAL利用不确定性估计算法，优先主动标注那些被预测为不确定的新类别样本，同时对每个类别的高置信度预测进行伪标注。在多个数据集、消融实验、设置和骨干网络（如ViT基础模型）上的评估表明该方法的有效性。代码将在论文接受后发布。
原文摘要 · Abstract (English)
AI deployed in many real-world use cases should be capable of adapting to novelties encountered after deployment. Here, we consider a challenging, under-explored and realistic continual adaptation problem: a deployed AI agent is continuously provided with unlabeled data that may contain not only unseen samples of known classes but also samples from novel (unknown) classes. In such a challenging setting, it has only a tiny labeling budget to query the most informative samples to help it continuously learn. We present a comprehensive solution to this complex problem with our model "CUAL" (Continual Uncertainty-aware Active Learner). CUAL leverages an uncertainty estimation algorithm to prioritize active labeling of ambiguous (uncertain) predicted novel class samples while also simultaneously pseudo-labeling the most certain predictions of each class. Evaluations across multiple datasets, ablations, settings and backbones (e.g. ViT foundation model) demonstrate our method's effectiveness. We will release our code upon acceptance.
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