发表机构
CNRS LAPP（法国国家科学研究中心 LAPP）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对极端标签偏移场景，提出关联域适应与多任务平衡的组合框架，在CTAO首台大型望远镜的物理场景中验证方法，对比相关技术并扩展重要性加权研究，相关资源开源发布。
AI 中文摘要
无监督域适应是一类广泛应用的方法，它利用带标注的源域知识训练模型，使其在相关的无标注目标域上表现良好。这类方法通常引入与域适应相关的辅助任务，该任务可整合至多任务范式中，多任务范式旨在将多个单任务模型合并为统一架构。本文中，我们提出在极端类别不平衡的真实场景下关联域适应与多任务平衡，因此构建了一个组合框架以覆盖并验证这些方法，并在基于切伦科夫望远镜阵列天文台（CTAO）的物理场景中评估其性能。我们对部分相关适应技术开展了对比研究，强调了极端标签偏移的影响，并扩展了关于重要性加权以纠正该问题的研究。完整代码与结果已作为开源资源发布于Zenodo平台，可供获取。
英文摘要
Unsupervised domain adaptation is a widespread set of methods that leverages the knowledge of a labeled source domain to train a model to perform well on a related unlabeled target domain. They generally introduce an auxiliary adaptation-related task that can be integrated into the multitask paradigm, which aims to merge multiple single-task models into a unified architecture. In this paper, we propose to associate domain adaptation and multitask balancing in the realistic context of an extreme class imbalance. Therefore, we propose a combined framework to cover and validate these approaches, and evaluate its performance in the physics-based context of the Cherenkov Telescope Array Observatory (CTAO). Along with a comparative study of some relevant adaptation techniques, we highlight the impact of extreme label shift and extend the investigations on importance weighting to rectify it. The complete code and results are published and available as open-source resources on Zenodo.
CommentsThis is the accepted version of the article published in Astronomy and Computing
