过度拟合
计算机科学
稳健性(进化)
机器学习
一般化
人工智能
钥匙(锁)
领域(数学分析)
利用
标签
源代码
标记数据
数据挖掘
数学
数学分析
生物化学
犯罪学
基因
化学
计算机安全
社会学
人工神经网络
操作系统
作者
Adnan Khan,Mai A. Shaaban,Muhammad Haris Khan
出处
期刊:Cornell University - arXiv
日期:2024-01-01
被引量:1
标识
DOI:10.48550/arxiv.2401.13965
摘要
Beyond attaining domain generalization (DG), visual recognition models should also be data-efficient during learning by leveraging limited labels. We study the problem of Semi-Supervised Domain Generalization (SSDG) which is crucial for real-world applications like automated healthcare. SSDG requires learning a cross-domain generalizable model when the given training data is only partially labelled. Empirical investigations reveal that the DG methods tend to underperform in SSDG settings, likely because they are unable to exploit the unlabelled data. Semi-supervised learning (SSL) shows improved but still inferior results compared to fully-supervised learning. A key challenge, faced by the best-performing SSL-based SSDG methods, is selecting accurate pseudo-labels under multiple domain shifts and reducing overfitting to source domains under limited labels. In this work, we propose new SSDG approach, which utilizes a novel uncertainty-guided pseudo-labelling with model averaging (UPLM). Our uncertainty-guided pseudo-labelling (UPL) uses model uncertainty to improve pseudo-labelling selection, addressing poor model calibration under multi-source unlabelled data. The UPL technique, enhanced by our novel model averaging (MA) strategy, mitigates overfitting to source domains with limited labels. Extensive experiments on key representative DG datasets suggest that our method demonstrates effectiveness against existing methods. Our code and chosen labelled data seeds are available on GitHub: https://github.com/Adnan-Khan7/UPLM
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