Results 1 to 10 of about 7,740,063 (200)
Meta Learning for Few-Shot One-Class Classification
AI, 2021 We propose a method that can perform one-class classification given only a small number of examples from the target class and none from the others. We formulate the learning of meaningful features for one-class classification as a meta-learning problem ...
Gabriel Dahia +1 more
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Universal Framework for Joint Image Restoration and 3D Body Reconstruction
IEEE Access, 2021 Recent works have demonstrated excellent state-of-the-art achievements in image restoration and 3D body reconstruction from an input image. The 3D body reconstruction task, however, relies heavily on the input image’s quality.
Jonathan Samuel Lumentut +5 more
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Few-shot EEG sleep staging based on transductive prototype optimization network
Frontiers in Neuroinformatics, 2023 Electroencephalography (EEG) is a commonly used technology for monitoring brain activities and diagnosing sleep disorders. Clinically, doctors need to manually stage sleep based on EEG signals, which is a time-consuming and laborious task. In this study,
Jingcong Li +3 more
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The Internet of Federated Things (IoFT)
IEEE Access, 2021 The Internet of Things (IoT) is on the verge of a major paradigm shift. In the IoT system of the future, IoFT, the “cloud” will be substituted by the “crowd” where model training is brought to the edge, allowing IoT devices to
Raed Kontar +14 more
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Applied Sciences, 2023 Deep learning-based Automatic Modulation Classification (AMC) has achieved excellent classification accuracy. However, most deep learning-based AMC methods have an inherent drawback.
Hanhui Yang +4 more
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Meta-Strategy for Learning Tuning Parameters with Guarantees
Entropy, 2021 Online learning methods, similar to the online gradient algorithm (OGA) and exponentially weighted aggregation (EWA), often depend on tuning parameters that are difficult to set in practice.
Dimitri Meunier, Pierre Alquier
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Mathematics, 2022 In the supervised classification area, the algorithm selection problem (ASP) refers to determining the a priori performance of a given classifier in some specific problem, as well as the finding of which is the most suitable classifier for some tasks ...
Claudia C. Tusell-Rey +5 more
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Few-Shot User-Adaptable Radar-Based Breath Signal Sensing
Sensors, 2023 Vital signs estimation provides valuable information about an individual’s overall health status. Gathering such information usually requires wearable devices or privacy-invasive settings.
Gianfranco Mauro +5 more
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Looking Back to Lower-Level Information in Few-Shot Learning
Information, 2020 Humans are capable of learning new concepts from small numbers of examples. In contrast, supervised deep learning models usually lack the ability to extract reliable predictive rules from limited data scenarios when attempting to classify new examples ...
Zhongjie Yu, Sebastian Raschka
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Research Progress on Few-Shot Learning for Remote Sensing Image Interpretation
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 The rapid development of deep learning brings effective solutions for remote sensing image interpretation. Training deep neural network models usually require a large number of manually labeled samples. However, there is a limitation to obtain sufficient
Xian Sun +5 more
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