Machine learning models deployed in open-world scenarios often encounter unfamiliar conditions and perform poorly in unanticipated situations. As AI systems advance and find application in safety-critical domains, effectively handling out-of-distribution (OOD) data is crucial to building open-world learning systems. In this work, we introduce ALOE, a novel active learning algorithm for open-world environments designed to enhance model adaptation by incorporating new OOD classes via a two-stage approach. First, diversity sampling selects a representative set of examples, followed by energy-based OOD detection to prioritize likely unknown classes for annotation. This strategy accelerates class discovery and learning, even under constrained annotation budgets. Evaluations on three long-tailed image classification benchmarks demonstrate that ALOE outperforms traditional active learning baselines, effectively expanding known categories while balancing annotation cost. Our findings reveal a crucial tradeoff between enhancing known-class performance and discovering new classes, setting the stage for future advancements in open-world machine learning.

本研究解决了机器学习模型在开放世界中遭遇未知情况时性能不佳的问题。我们提出了一种名为ALOE的新型主动学习算法，通过两阶段方法提升模型适应性，有效整合新的未知类别。研究结果表明，ALOE能够在有限的注释预算下，加速类别发现与学习，并在已知类别性能和新类别发现之间实现重要平衡，为开放世界机器学习的未来发展奠定基础。

开放世界中的深度主动学习