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转移注释器和依赖实例的过渡矩阵来从人群中学习

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    科学领域:

    • 机器学习 机器学习
    • 数据科学数据科学数据科学
    • 计算机视觉 计算机视觉

    背景情况:

    • 众包数据注释涉及多个注释者,导致频繁的标签错误.
    • 使用噪声过渡矩阵建模标签噪声对于数据质量至关重要.
    • 由于注释稀疏性,现有的方法难以应对注释器和实例依赖过渡矩阵 (AIDTM) 的复杂性.

    研究的目的:

    • 开发一种实际的方法来估计一般的AIDTM.
    • 克服现有方法的局限性,这些方法假定实例独立性或使用简化参数模型.
    • 提高众包数据注释的准确性和稳定性.

    主要方法:

    • 使用深度神经网络进行AIDTM参数化,以保持建模通用性.
    • 通过模拟注释者噪音模式的混合,并将其传递给单个注释者,采用知识传递.
    • 利用邻近的注释器之间的知识传输来校准建模,并减轻不同噪音模式的注释器之间的干扰.

    主要成果:

    • 理论分析证实了全球对个人和邻国对邻国知识转移在建模AIDTM中的有效性.
    • 实验结果表明,拟议方法在合成和现实世界众包数据集上的优越性.
    • 该方法成功地解决了在估计复杂噪声过渡矩阵时注释稀疏性的挑战.

    结论:

    • 提出的基于深度神经网络的方法与知识传输有效地模拟了一般的AIDTM.
    • 这项工作为处理群众来源数据注释中的标签噪声提供了更强大的解决方案.
    • 这些发现对提高在众包数据上训练的机器学习模型的质量和可靠性有重大影响.