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DISTANCE METRIC LEARNING WITH N-PAIR LOSS

机译:N对损失的距离度量学习

摘要

A method includes receiving N pairs of training examples and class labels therefor. Each pair includes a respective anchor example, and a respective non-anchor example capable of being a positive or a negative training example. The method further includes extracting features of the pairs by applying a DHCNN, and calculating, for each pair based on the features, a respective similarly measure between the respective anchor and no example. The method additionally includes calculating a similarity score based on the respective similarity measure for each pair. The score represents similarities between all anchor points and positive training examples in the pairs relative to similarities between all anchor points and negative training examples in the pairs. The method further includes maximizing the similarity score for the anchor example for each pair to pull together the training examples from a same class while pushing apart the training examples from different classes.
机译:一种方法包括接收N对训练示例及其分类标签。每对包括各自的锚示例和能够作为正面或负面训练示例的各自的非锚示例。该方法进一步包括通过应用DHCNN来提取对的特征,并且基于所述特征针对每个对计算相应锚点之间的相应相似度量,而不计算示例。该方法还包括基于每一对的相应相似性度量来计算相似性得分。分数表示成对的所有锚定点和正训练示例之间的相似性,相对于成对的所有锚定点和负训练示例之间的相似性。该方法还包括使每对锚定示例的相似性得分最大化,以将来自同一类别的训练示例拉到一起,同时将来自不同类别的训练示例推开。

著录项

  • 公开/公告号US2017228641A1

    专利类型

  • 公开/公告日2017-08-10

    原文格式PDF

  • 申请/专利权人 NEC LABORATORIES AMERICA INC.;

    申请/专利号US201615385283

  • 发明设计人 KIHYUK SOHN;

    申请日2016-12-20

  • 分类号G06N3/08;G06N7;G06F17/11;G06N3/04;

  • 国家 US

  • 入库时间 2022-08-21 13:48:03

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