面向计算机视觉的深度度量学习：简要综述
Deep Metric Learning for Computer Vision: A Brief Overview
AI总结：
针对交叉熵损失在数据类内方差大、类间方差小时效果不佳的问题，本文综述了深度度量学习领域的最新进展，该方法通过学习嵌入表示、结合采样策略与损失函数生成判别性嵌入空间以衡量样本相似性。
摘要：
优化深度神经网络的目标函数在构建输入数据的增强特征表示方面发挥着关键作用。尽管基于交叉熵的损失公式已被广泛应用于各类有监督深度学习任务中，但当输入数据分布存在类内方差大、类间方差小的情况时，这类方法往往效果欠佳。深度度量学习（Deep Metric Learning）致力于开发相关方法，通过学习一种表示函数，将数据样本映射到具有代表性的嵌入空间，以此衡量数据样本间的相似性。它借助精心设计的采样策略和损失函数，即便在类间方差低、类内方差高的数据分布下，也能助力优化生成具有判别性的嵌入空间。本章将概述该领域的最新进展，并探讨当前最先进的深度度量学习方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
