基于交互式对比学习的带噪声部分标签学习的样本选择

IF 7.5 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Xiaotong Yu , Shiding Sun , Yingjie Tian
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引用次数: 0

摘要

在弱监督学习的上下文中,部分标签学习(PLL)解决了每个训练实例与一组部分标签相关联的情况,其中只有一个是准确的。然而,在复杂的现实世界任务中,限制性假设可能无效,这意味着基本事实可能在候选标签集之外。在这项工作中,我们摆脱了约束,解决了锁相环的噪声标签问题。首先,我们引入了一种选择策略,使深度模型能够通过翻转图像和原始图像的损失值来选择干净的样本。此外,我们逐步识别所选样本的真实标签,并集成两个模型以获取未选择样本的知识。为了提取更好的特征表示,我们引入了伪标记交互对比学习来聚合所有样本的跨网络信息。实验结果验证了我们的方法在具有不同水平标签噪声的噪声锁相环任务上优于基线方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Sample selection for noisy partial label learning with interactive contrastive learning
In the context of weakly supervised learning, partial label learning (PLL) addresses situations where each training instance is associated with a set of partial labels, with only one being accurate. However, in complex realworld tasks, the restrictive assumption may be invalid which means the ground-truth may be outside the candidate label set. In this work, we loose the constraints and address the noisy label problem for PLL. First, we introduce a selection strategy, which enables deep models to select clean samples via the loss values of flipped and original images. Besides, we progressively identify the true labels of the selected samples and ensemble two models to acquire the knowledge of unselected samples. To extract better feature representations, we introduce pseudo-labeled interactive contrastive learning to aggregate cross-network information of all samples. Experimental results verify that our approach surpasses baseline methods on noisy PLL task with different levels of label noise.
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来源期刊
Pattern Recognition
Pattern Recognition 工程技术-工程:电子与电气
CiteScore
14.40
自引率
16.20%
发文量
683
审稿时长
5.6 months
期刊介绍: The field of Pattern Recognition is both mature and rapidly evolving, playing a crucial role in various related fields such as computer vision, image processing, text analysis, and neural networks. It closely intersects with machine learning and is being applied in emerging areas like biometrics, bioinformatics, multimedia data analysis, and data science. The journal Pattern Recognition, established half a century ago during the early days of computer science, has since grown significantly in scope and influence.
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