Deep Metric Learning for Computer Vision: A Brief Overview
Deen Dayal Mohan Bhavin Jawade Srirangaraj Setlur Venu Govindaraj
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.