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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.
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...
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.
23 de jul. 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.
1 de jun. de 2023 · In this paper, we provide an up-to-date review of deep metric learning methods for few-shot image classification from 2018 to 2022 and categorize them into three groups according to three stages of metric learning, namely learning feature embeddings, learning class representations, and learning distance measures.
Bayesian metric learning, information theoretic methods, and empirical risk minimization in met-ric learning. In deep learning methods, we first introduce reconstruction autoencoders and super-vised loss functions for metric learning. Then, Siamese networks and its various loss functions, triplet mining, and triplet sampling are explained.
3 Altmetric. Abstract. We present a novel hierarchical triplet loss (HTL) capable of automatically collecting informative training samples (triplets) via a defined hierarchical tree that encodes global context information.
