Out-of-distribution (OOD) learning often relies heavily on statistical approaches or predefined assumptions about OOD data distributions, hindering their efficacy in addressing multifaceted challenges of OOD generalization and OOD detection in real-world deployment environments. This paper presents a novel framework for OOD learning with human feedback, which can provide invaluable insights into the nature of OOD shifts and guide effective model adaptation. Our framework capitalizes on the freely available unlabeled data in the wild that captures the environmental test-time OOD distributions under both covariate and semantic shifts. To harness such data, our key idea is to selectively provide human feedback and label a small number of informative samples from the wild data distribution, which are then used to train a multi-class classifier and an OOD detector. By exploiting human feedback, we enhance the robustness and reliability of machine learning models, equipping them with the capability to handle OOD scenarios with greater precision. We provide theoretical insights on the generalization error bounds to justify our algorithm. Extensive experiments show the superiority of our method, outperforming the current state-of-the-art by a significant margin.

翻译：分布外学习通常严重依赖统计方法或关于分布外数据分布的预设假设，这阻碍了其在现实部署环境中应对分布外泛化与分布外检测的多方面挑战时的有效性。本文提出一种基于人类反馈的分布外学习新框架，该框架能够为分布外偏移的本质提供宝贵洞见，并指导有效的模型适应。我们的框架充分利用了自然环境中可自由获取的未标注数据，这些数据在协变量偏移和语义偏移下捕捉了环境测试时的分布外分布。为利用此类数据，我们的核心思想是选择性提供人类反馈，并从自然数据分布中标注少量信息丰富的样本，随后用这些样本训练一个多类分类器和一个分布外检测器。通过利用人类反馈，我们增强了机器学习模型的鲁棒性与可靠性，使其能够以更高精度处理分布外场景。我们提供了关于泛化误差界的理论分析以证明算法的合理性。大量实验表明，我们的方法显著优于当前最先进技术，展现出卓越性能。