V2EX multiple instance learning

Multiple Instance Learning

释义 Definition

多示例学习(MIL):一种机器学习设定/方法。训练数据不是单个样本,而是由多个样本组成的“包(bag)”。通常只给每个包一个标签:例如正包表示包里至少有一个样本为正;负包表示包里所有样本都为负。常用于弱监督场景,如医学影像、目标检测、文本与音频片段识别等。

发音 Pronunciation (IPA)

/mltpl nstns lrn/

例句 Examples

Multiple instance learning is useful when only bag-level labels are available.
当只有“包级别”的标签可用时,多示例学习很有用。

We trained a multiple instance learning model to identify malignant regions in whole-slide images, even though the annotations were provided only for each slide as a whole.
我们训练了一个多示例学习模型来在全切片图像中识别恶性区域,尽管标注只提供到了每张切片整体的层面。

词源 Etymology

“Multiple instance learning”由 multiple(多个)+ instance(实例/样本)+ learning(学习)构成。该术语机器学习研究中用于描述一种“以样本集合(包)为单位监督”的学习框架,强调训练信号来自集合层面而非单个样本层面,因此常被归入弱监督学习相关方法。

相关词 Related Words

文献与著作中的用例 Literary / Notable Works

  • Dietterich, T. G., Lathrop, R. H., & Lozano-Pérez, T. (1997). Solving the multiple instance problem with axis-parallel rectangles.
  • Maron, O., & Lozano-Pérez, T. (1998). A framework for multiple-instance learning.
  • Andrews, S., Tsochantaridis, I., & Hofmann, T. (2002). Support vector machines for multiple-instance learning.
  • Zhou, Z.-H., & Zhang, M.-L. (2002). Neural networks for multiple-instance learning.
  • Carbonneau, M.-A., Cheplygina, V., Granger, E., & Gagnon, G. (2018). Multiple instance learning: A survey of problem characteristics and applications.
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