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5 de jul. de 2023 · Loss Functions and Metrics in Deep Learning. One of the essential components of deep learning is the choice of the loss function and performance metrics used to train and evaluate models. This paper reviews the most prevalent loss functions and performance measurements in deep learning.
5 de jul. de 2023 · One of the essential components of deep learning is the choice of the loss function and performance metrics used to train and evaluate models. This paper reviews the most prevalent loss...
13 de mar. de 2024 · Metrics. Abstract. Research on different machine learning (ML) has become incredibly popular during the past few decades. However, for some researchers not familiar with statistics, it might...
This book presents the current mathematical understanding of deep learning methods from the point of view of the leading experts in the field. It serves both as a starting point for researchers and graduate students in computer science, mathematics, and statistics trying to get into the field and as an invaluable reference for future research.
19 de dic. de 2023 · In this chapter, we will provide an overview of recent progress in this area and discuss state-of-the-art Deep Metric Learning approaches. Keywords: Deep Metric Learning, Triplet Loss, Image Retrieval, Face Verifica-tion, Person Re-Identification.
21 de ago. de 2019 · Metric learning aims to measure the similarity among samples while using an optimal distance metric for learning tasks. Metric learning methods, which generally use a linear projection, are limited in solving real-world problems demonstrating non-linear characteristics. Kernel approaches are utilized in metric learning to address this problem.
1 de jun. de 2023 · A detailed review of latest deep metric learning methods for few-shot image classification. •. More than 60 methods published from 2018 to 2022 are surveyed. •. Methods are divided according to three stages of deep metric learning. •. Challenges and future directions of few-shot image classification are discussed.
