Objective functions that optimize deep neural networks play a vital role in
creating an enhanced feature representation of the input data. Although
cross-entropy-based loss formulations have been extensively used in a variety
of supervised deep-learning applications, these methods tend to be less
adequate when there is large intra-class variance and low inter-class variance
in input data distribution. Deep Metric Learning seeks to develop methods that
aim to measure the similarity between data samples by learning a representation
function that maps these data samples into a representative embedding space. It
leverages carefully designed sampling strategies and loss functions that aid in
optimizing the generation of a discriminative embedding space even for
distributions having low inter-class and high intra-class variances. In this
chapter, we will provide an overview of recent progress in this area and
discuss state-of-the-art Deep Metric Learning approaches.

通过深度度学习，优化深度神经网络的目标函数在创建输入数据的增强特征表示中起着至关重要的作用。然而，基于交叉熵的损失函数对于具有大的类内差异和低的类间差异的输入数据分布来说往往不够充分。深度度量学习旨在通过学习将数据样本映射到代表性嵌入空间的表示函数，以度量数据样本之间的相似性。它利用精心设计的采样策略和损失函数，帮助优化产生具有低类间和高类内方差的区分性嵌入空间的生成。在本章中，我们将概述该领域最新的进展，并讨论最先进的深度度量学习方法。