Abstract Deep metric learning (DML) has been designed to maximize the inter-class variance that is the distance between embedding features belonging to different classes. Since conventional DML techniques do not consider the statistical characteristics of the embedding space, or they calculate similarity using only a given feature, they make it difficult to adaptively reflect the characteristics of the feature distribution during the learning process. This paper proposes a virtual metric loss (VML) incorporating with embedding features by using virtual samples produced through linear discriminant analysis (LDA). This study is valuable in that it proposes a new metric that can learn inter-class variance of embedding features by integrating discriminant analysis and metric learning which have a common purpose of inter-class variance analysis. In addition, we theoretically analyze the eigenvalue equation problem and the degree of stabilization in the embedding space. We have verified the performance of the proposed VML through extensive experiments on large and few-shot retrieval datasets. For example, in the CUB200-2011 dataset, the VML showed a recall rate about 0.7% higher than a state-of-the-art method. We also explored a new similarity through virtual samples and adjusted the difficulty of embedding features, thereby confirming the possibility of expanding virtual samples into various fields of pattern recognition.

中文翻译：

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使用判别分析的基于虚拟样本的深度度量学习

摘要 深度度量学习 (DML) 旨在最大化类间方差，即属于不同类的嵌入特征之间的距离。由于传统的 DML 技术没有考虑嵌入空间的统计特征，或者它们仅使用给定的特征计算相似度，因此它们很难在学习过程中自适应地反映特征分布的特征。本文通过使用通过线性判别分析 (LDA) 生成的虚拟样本，提出了一种结合嵌入特征的虚拟度量损失 (VML)。这项研究的价值在于它提出了一种新的度量，可以通过集成具有类间方差分析的共同目的的判别分析和度量学习来学习嵌入特征的类间方差。此外，我们从理论上分析了特征值方程问题和嵌入空间中的稳定程度。我们已经通过对大型和少镜头检索数据集的广泛实验验证了所提出的 VML 的性能。例如，在 CUB200-2011 数据集中，VML 显示的召回率比最先进的方法高约 0.7%。我们还通过虚拟样本探索了一种新的相似性，并调整了嵌入特征的难度，从而确认了将虚拟样本扩展到模式识别各个领域的可能性。在 CUB200-2011 数据集中，VML 的召回率比最先进的方法高约 0.7%。我们还通过虚拟样本探索了一种新的相似性，并调整了嵌入特征的难度，从而确认了将虚拟样本扩展到模式识别各个领域的可能性。在 CUB200-2011 数据集中，VML 的召回率比最先进的方法高约 0.7%。我们还通过虚拟样本探索了一种新的相似性，并调整了嵌入特征的难度，从而确认了将虚拟样本扩展到模式识别各个领域的可能性。