Results 11 to 20 of about 510,572 (297)
LMDAPNet: A Novel Manifold-Based Deep Learning Network
IEEE Access, 2020 In this paper, we propose a novel deep learning network, called Local Manifold Discriminant Analysis Projection Network (LMDAPNet). Different from most existing face recognition based on Deep Neural Networks (DNNs) methods that learn complex feature ...
Yan Li, Guitao Cao, Wenming Cao
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A Deep Convolutional Neural Network With Multiscale Feature Dynamic Fusion for InSAR Phase Filtering
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022 Interferometric phase filtering is a crucial step in the interferometric synthetic aperture radar (InSAR) data processing, which is also important for improving the accuracy of topography mapping and deformation monitoring.
Wang Yang +8 more
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Statistical Process Control with Intelligence Based on the Deep Learning Model
Applied Sciences, 2019 Statistical process control (SPC) is an important tool of enterprise quality management. It can scientifically distinguish the abnormal fluctuations of product quality.
Tao Zan +5 more
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A Clustering Ensemble Method of Aircraft Trajectory Based on the Similarity Matrix
Aerospace, 2022 Performing clustering analysis on a large amount of historical trajectory data can obtain information such as frequent flight patterns of aircraft and air traffic flow distribution, which can provide a reference for the revision of standard flight ...
Xiao Chu, Xianghua Tan, Weili Zeng
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Learning Distinct Features Helps, Provably
, 2023 This work has been supported by the NSF-Business Fin- land Center for Visual and Decision Informatics (CVDI) project AMALIA. The work of Jenni Raitoharju was funded by the Academy of Finland (project 324475). Alexandros Iosifidis acknowledges funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No ...
Laakom, Firas +4 more
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Dataset2Vec: learning dataset meta-features [PDF]
Data Mining and Knowledge Discovery, 2021 AbstractMeta-learning, or learning to learn, is a machine learning approach that utilizes prior learning experiences to expedite the learning process on unseen tasks. As a data-driven approach, meta-learning requires meta-features that represent the primary learning tasks or datasets, and are estimated traditonally as engineered dataset statistics that
Hadi S. Jomaa +2 more
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How deep convolutional neural networks lose spatial information with training
Machine Learning: Science and Technology, 2023 A central question of machine learning is how deep nets manage to learn tasks in high dimensions. An appealing hypothesis is that they achieve this feat by building a representation of the data where information irrelevant to the task is lost.
Umberto M Tomasini +3 more
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Remote Sensing, 2021 Scene understanding of remote sensing images is of great significance in various applications. Its fundamental problem is how to construct representative features.
Jian Peng +5 more
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Journal of Cybersecurity and Privacy, 2023 Session-replay bots are believed to be the latest and most sophisticated generation of web bots, and they are also very difficult to defend against. Combating session-replay bots is particularly challenging in online domains that are repeatedly visited ...
Shadi Sadeghpour, Natalija Vlajic
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Learning Covariant Feature Detectors [PDF]
, 2016 Local covariant feature detection, namely the problem of extracting viewpoint invariant features from images, has so far largely resisted the application of machine learning techniques. In this paper, we propose the first fully general formulation for learning local covariant feature detectors.
Lenc, Karel, Vedaldi, Andrea
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