The learning to defer (L2D) framework allows autonomous systems to be safe and robust by allocating difficult decisions to a human expert. All existing work on L2D assumes that each expert is well-identified, and if any expert were to change, the system should be re-trained. In this work, we alleviate this constraint, formulating an L2D system that can cope with never-before-seen experts at test-time. We accomplish this by using meta-learning, considering both optimization- and model-based variants. Given a small context set to characterize the currently available expert, our framework can quickly adapt its deferral policy. For the model-based approach, we employ an attention mechanism that is able to look for points in the context set that are similar to a given test point, leading to an even more precise assessment of the expert's abilities. In the experiments, we validate our methods on image recognition, traffic sign detection, and skin lesion diagnosis benchmarks.

翻译：学习型委托（L2D）框架允许自主系统通过将困难决策分配给人类专家来实现安全性与鲁棒性。现有L2D研究均假设每个专家身份明确，且若专家发生变动，系统需重新训练。本研究突破这一限制，提出可应对测试阶段未见过的专家的L2D系统。我们采用元学习方法，分别探讨基于优化和基于模型的变体。给定用于描述当前可用专家的少量上下文样本集，本框架可快速调整其委托策略。针对基于模型的方法，我们采用注意力机制，通过寻找上下文样本中与待测样本相似的点，实现更精确的专家能力评估。实验部分，我们在图像识别、交通标志检测和皮肤病变诊断基准上验证了所提方法的有效性。