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.

学习推迟（L2D）框架通过将困难决策交给人类专家，实现了自主系统的安全性和稳健性。我们在此工作中缓解了每个专家都是明确识别的假设，并设计了一个能够应对测试时从未遇到过的专家的 L2D 系统。我们使用元学习来实现这一目标，同时考虑了基于优化和基于模型的变种。通过给出用于描述当前可用专家的小的上下文集合，我们的框架可以快速适应其推迟策略。对于基于模型的方法，我们采用了一种注意机制，可以查找上下文集合中与给定测试点相似的点，从而更精确地评估专家的能力。在实验中，我们在图像识别、交通标志检测和皮肤病病变诊断基准上验证了我们的方法。