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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...
5 de jul. de 2023 · This paper reviews the most prevalent loss functions and performance measurements in deep learning. We examine the benefits and limits of each technique and illustrate their application to various deep-learning problems.
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.
9 de jul. de 2023 · Other metrics such as ROC\AUC and Kappa statistics have proven to provide additional insightful into the effectiveness of an algorithms adequacy and should also be considered when evaluating the effectiveness of binary, multi-class and multi-labelled classifiers.
19 de dic. de 2023 · 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.
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.
As an effective way to learn a distance metric be-tween pairs of samples, deep metric learning (DML) has drawn significant attention in recent years. The key idea of DML is to learn a set of hierarchical non-linear mappings using deep neural networks, and then project the data samples into a new feature space for comparing or matching.
