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Pacing Electrocardiogram Detection With Memory-Based Autoencoder and Metric Learning
Frontiers in Physiology, 2021 Remote ECG diagnosis has been widely used in the clinical ECG workflow. Especially for patients with pacemaker, in the limited information of patient's medical history, doctors need to determine whether the patient is wearing a pacemaker and also ...
Zhaoyang Ge +9 more
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Learning Neighborhoods for Metric Learning [PDF]
, 2012 Metric learning methods have been shown to perform well on different learning tasks. Many of them rely on target neighborhood relationships that are computed in the original feature space and remain fixed throughout learning. As a result, the learned metric reflects the original neighborhood relations.
Wang Jun +2 more
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Adversarial Metric Learning [PDF]
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018 In the past decades, intensive efforts have been put to design various loss functions and metric forms for metric learning problem. These improvements have shown promising results when the test data is similar to the training data. However, the trained models often fail to produce reliable distances on the ambiguous test pairs due to the different ...
Shuo Chen 0003 +5 more
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Metrics and Continuity in Reinforcement Learning
Proceedings of the AAAI Conference on Artificial Intelligence, 2021 In most practical applications of reinforcement learning, it is untenable to maintain direct estimates for individual states; in continuous-state systems, it is impossible. Instead, researchers often leverage {\em state similarity} (whether explicitly or implicitly) to build models that can generalize well from a limited set of samples.
Charline Le Lan +2 more
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IEEE Access, 2021 Many object re-identification (Re-ID) methods that depend on large-scale training datasets have been proposed in recent years. However, the performance of these methods degrades dramatically when insufficient training data are available.
Sheng-Hung Fan +3 more
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Object Tracking With Structured Metric Learning
IEEE Access, 2019 In this paper, we propose a novel tracking method based on structured metric learning, which takes the advantages of both structured learning and distance metric learning.
Xiaolin Zhao +4 more
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Gong-kuang zidonghua, 2023 In the traditional personnel re-recognition method in coal mine underground based on metric learning, because metric learning ignores the absolute distance between positive and negative samples, the gradient of the loss function disappears or disperses ...
ZHANG Liya, WANG Yu, HAO Bonan
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Multi-Sensors System and Deep Learning Models for Object Tracking
Sensors, 2023 Autonomous navigation relies on the crucial aspect of perceiving the environment to ensure the safe navigation of an autonomous platform, taking into consideration surrounding objects and their potential movements. Consequently, a fundamental requirement
Ghina El Natour +2 more
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A Multi-Layer Feature Fusion Method for Few-Shot Image Classification
Sensors, 2023 In image classification, few-shot learning deals with recognizing visual categories from a few tagged examples. The degree of expressiveness of the encoded features in this scenario is a crucial question that needs to be addressed in the models being ...
Jacó C. Gomes +2 more
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