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Significance of Softmax-Based Features in Comparison to Distance Metric Learning-Based Features

机译:基于Softmax的特征与基于距离度量学习的特征的意义

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摘要

End-to-end distance metric learning (DML) has been applied to obtain features useful in many computer vision tasks. However, these DML studies have not provided equitable comparisons between features extracted from DML-based networks and softmax-based networks. In this paper, we present objective comparisons between these two approaches under the same network architecture.
机译:已应用端到端距离度量学习(DML)以获得有用的功能在许多计算机视觉任务中。然而,这些DML研究没有提供从基于DML的网络和基于SoftMax网络中提取的特征之间的公平比较。在本文中,我们在同一网络架构下呈现这两种方法之间的客观比较。

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