Zobrazeno 1 - 10
of 1 997
pro vyhledávání: '"Semisupervised learning"'
Publikováno v:
Energy Science & Engineering, Vol 12, Iss 4, Pp 1356-1368 (2024)
Abstract Globally, coal is a critical energy source, and the profits of related enterprises are highly related to changes in the coal price. A robust coal purchasing cost forecasting method may enhance the coal purchasing strategies of coal‐consumi
Externí odkaz:
https://doaj.org/article/8c5fbe0ee1354e82a60a8ba54c4b9678
Autor:
Mohamed Sami Nafea, Zool Hilmi Ismail
Publikováno v:
IEEE Access, Vol 12, Pp 162251-162266 (2024)
Electroencephalogram (EEG) data annotation demands considerable expertise and is a time-intensive process. Moreover, inter-subject variability intensifies the challenge of domain shift, adversely impacting the generalization performance of deep learn
Externí odkaz:
https://doaj.org/article/95678ee4415246cc9d023755184229dc
Autor:
Ziqian Tan, Chen Wu
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 15635-15650 (2024)
Vehicle detection is vital for urban planning and traffic management. Optical remote sensing imagery, known for its high resolution and extensive coverage, is ideal for this task. Traditional horizontal bounding box (HBB) annotations often include ex
Externí odkaz:
https://doaj.org/article/198911ab628e4f6cb49e3d1be5839513
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 6426-6443 (2024)
Hyperspectral images are valuable for precise land cover change detection in a consistent area over time. Nevertheless, supervised methods for hyperspectral change detection face limitations due to insufficient labeled samples. Additionally, current
Externí odkaz:
https://doaj.org/article/799e15002f0a4341b9c87d1f4b6ff858
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 5193-5203 (2024)
Synthetic aperture radar (SAR) image target detection methods based on semisupervised learning, such as the mean teacher framework, have shown promise in diminishing the issue of limited labeled data. However, several challenges exist in current meth
Externí odkaz:
https://doaj.org/article/fd359cadb91e4bb79b11ac8080a17999
Publikováno v:
ETRI Journal, Vol 45, Iss 6, Pp 1007-1021 (2023)
Stroke is the leading cause of permanent disability in adults, and it can cause permanent brain damage. According to the World Health Organization, 795,000 Americans experience a new or recurrent stroke each year. Early detection of medical disorders
Externí odkaz:
https://doaj.org/article/37fa72ae74ba4745af3a49512402d62e
Publikováno v:
CAAI Transactions on Intelligence Technology, Vol 8, Iss 4, Pp 1258-1273 (2023)
Abstract Most unsupervised or semisupervised hyperspectral anomaly detection (HAD) methods train background reconstruction models in the original spectral domain. However, due to the noise and spatial resolution limitations, there may be a lack of di
Externí odkaz:
https://doaj.org/article/88e24a098f5a46d9ba47f7ebefa1e003
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 16, Pp 8353-8362 (2023)
Semisupervised semantic segmentation of remote sensing images has been proven to be an effective approach to reduce manual annotation costs and leverage available unlabeled data to improve segmentation performance. However, some existing methods that
Externí odkaz:
https://doaj.org/article/3cbf84fbc31446e2adbaf82b21a20acb
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 16, Pp 7838-7849 (2023)
Recently, convolutional neural networks (CNNs) have been widely applied in the seismic facies classification. However, even state-of-the-art CNN architectures often encounter classification confusion distinguishing seismic facies at their boundaries.
Externí odkaz:
https://doaj.org/article/be9182a5608040ae85cf4987ef955664
Autor:
Haoxiang Shi, Tetsuya Sakai
Publikováno v:
IEEE Access, Vol 11, Pp 84134-84143 (2023)
Contrastive learning is a promising approach to unsupervised learning, as it inherits the advantages of well-studied deep models without a dedicated and complex model design. In this paper, based on bidirectional encoder representations from transfor
Externí odkaz:
https://doaj.org/article/81e700f5a315419480e39cfe19355455