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.
@article{arxiv.2312.10046,
title = {Deep Metric Learning for Computer Vision: A Brief Overview},
author = {Deen Dayal Mohan and Bhavin Jawade and Srirangaraj Setlur and Venu Govindaraj},
journal= {arXiv preprint arXiv:2312.10046},
year = {2023}
}
Comments
Book Chapter Published In Handbook of Statistics, Special Issue - Deep Learning 48, 59