تقرير
Deep Metric Learning for Computer Vision: A Brief Overview
| العنوان: | Deep Metric Learning for Computer Vision: A Brief Overview |
|---|---|
| المؤلفون: | Mohan, Deen Dayal, Jawade, Bhavin, Setlur, Srirangaraj, Govindaraj, Venu |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Information Retrieval, Computer Science - Machine Learning |
| الوصف: | 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. Comment: Book Chapter Published In Handbook of Statistics, Special Issue - Deep Learning 48, 59 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2312.10046 |
| رقم الأكسشن: | edsarx.2312.10046 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |