Results 11 to 20 of about 762,608 (309)
Geometric Metric Learning for Multi-Output Learning
Mathematics, 2022 Due to its wide applications, multi-output learning that predicts multiple output values for a single input at the same time is becoming more and more attractive.
Huiping Gao, Zhongchen Ma
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Lifelong Metric Learning [PDF]
IEEE Transactions on Cybernetics, 2019 The state-of-the-art online learning approaches are only capable of learning the metric for predefined tasks. In this paper, we consider a lifelong learning problem to mimic "human learning," i.e., endowing a new capability to the learned metric for a new task from new online samples and incorporating the previous experiences.
Gan Sun +5 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 ...
Jun Li +5 more
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A Local-to-Global Metric Learning Framework From the Geometric Insight
IEEE Access, 2020 Metric plays a key role in the description of similarity between samples. An appropriate metric for data can well represent their distribution and further promote the performance of learning tasks.
Yaxin Peng +3 more
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Multiple Cayley-Klein metric learning. [PDF]
PLoS ONE, 2017 As a specific kind of non-Euclidean metric lies in projective space, Cayley-Klein metric has been recently introduced in metric learning to deal with the complex data distributions in computer vision tasks.
Yanhong Bi, Bin Fan, Fuchao Wu
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Deep Multiple Metric Learning for Time Series Classification
IEEE Access, 2021 Effective distance metric plays an important role in time series classification. Metric learning, which aims to learn a data-adaptive distance metric to measure the distance among samples, has achieved promising results on time series classification ...
Zhi Chen +6 more
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Metric Learning for Keyword Spotting [PDF]
2021 IEEE Spoken Language Technology Workshop (SLT), 2021 The goal of this work is to train effective representations for keyword spotting via metric learning. Most existing works address keyword spotting as a closed-set classification problem, where both target and non-target keywords are predefined. Therefore, prevailing classifier-based keyword spotting systems perform poorly on non-target sounds which are
Minjae Lee +4 more
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Metric Learning for Image Registration [PDF]
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 Image registration is a key technique in medical image analysis to estimate deformations between image pairs. A good deformation model is important for high-quality estimates. However, most existing approaches use ad-hoc deformation models chosen for mathematical convenience rather than to capture observed data variation.
Niethammer, Marc +2 more
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Fault Diagnosis of Rolling Bearing Based on Modified Deep Metric Learning Method
Shock and Vibration, 2021 A novel fault diagnosis method of rolling bearing based on deep metric learning and Yu norm is proposed in this paper, which is called a deep metric learning method based on Yu norm (DMN-Yu).
Zengbing Xu +4 more
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REID (Research and Evaluation in Education), 2021 Nowadays, in the pandemic of COVID-19, e-learning systems have been widely used to facilitate teaching and learning processes between lecturers and students.
Sulis Sandiwarno
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