Open-Set Domain Generalization (OSDG) is a challenging task requiring models to accurately predict familiar categories while minimizing confidence for unknown categories to effectively reject them in unseen domains. While the OSDG field has seen considerable advancements, the impact of label noise--a common issue in real-world datasets--has been largely overlooked. Label noise can mislead model optimization, thereby exacerbating the challenges of open-set recognition in novel domains. In this study, we take the first step towards addressing Open-Set Domain Generalization under Noisy Labels (OSDG-NL) by constructing dedicated benchmarks derived from widely used OSDG datasets, including PACS and DigitsDG. We evaluate baseline approaches by integrating techniques from both label denoising and OSDG methodologies, highlighting the limitations of existing strategies in handling label noise effectively. To address these limitations, we propose HyProMeta, a novel framework that integrates hyperbolic category prototypes for label noise-aware meta-learning alongside a learnable new-category agnostic prompt designed to enhance generalization to unseen classes. Our extensive experiments demonstrate the superior performance of HyProMeta compared to state-of-the-art methods across the newly established benchmarks. The source code of this work is released at https://github.com/KPeng9510/HyProMeta.

翻译：开放集域泛化（OSDG）是一项具有挑战性的任务，要求模型在未见域中准确预测已知类别的同时，对未知类别保持较低的置信度以有效拒识。尽管OSDG领域已取得显著进展，但标签噪声——现实数据集中普遍存在的问题——的影响在很大程度上被忽视了。标签噪声可能误导模型优化，从而加剧新域中开放集识别的挑战。在本研究中，我们首次针对噪声标签下的开放集域泛化（OSDG-NL）问题展开研究，基于广泛使用的OSDG数据集（包括PACS和DigitsDG）构建了专用基准。通过整合标签去噪与OSDG方法中的技术，我们评估了基线方法，并揭示了现有策略在有效处理标签噪声方面的局限性。为克服这些局限，我们提出了HyProMeta——一个新颖的框架，该框架集成了用于标签噪声感知元学习的双曲类别原型，以及一个可学习的、与未知类别无关的提示，旨在增强对未见类别的泛化能力。我们的大量实验表明，在新建立的基准测试中，HyProMeta相比现有最先进方法展现出更优越的性能。本工作的源代码发布于https://github.com/KPeng9510/HyProMeta。