解决极端类别不平衡下的无监督域适应问题，提升真实场景多任务图像分析性能
Unsupervised Domain Adaptation for Multitask Image Analysis in Realistic Context with Extreme Label Shift; Application to the CTAO first Large Sized Telescope
- 将域适应与多任务学习结合，应对极端标签偏移挑战
- 在CTAO物理仿真数据上验证框架有效性，显著改善模型泛化能力
- 开源代码与结果，适用于高能天体物理等极端数据分布场景
无监督域适应通过利用已标注源域的知识，使模型在相关但未标注的目标域上表现良好。现有方法通常引入辅助适应任务，并融入多任务范式，以整合多个单任务模型为统一架构。本文针对真实场景中极端类别不平衡的问题，提出一种联合域适应与多任务平衡的框架。该方法在切伦科夫望远镜阵列观测（CTAO）的物理仿真背景下进行验证。通过对比多种适配技术，揭示了极端标签偏移的影响，并扩展了重要性加权方法以缓解此问题。完整代码与实验结果已开源发布于Zenodo。
原文摘要 · Abstract (English)
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.
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