Zobrazeno 1 - 10
of 635
pro vyhledávání: '"Pseudo-labeling"'
Publikováno v:
Complex & Intelligent Systems, Vol 10, Iss 6, Pp 7661-7679 (2024)
Abstract Recent advances in multi-view multi-label learning are often hampered by the prevalent challenges of incomplete views and missing labels, common in real-world data due to uncertainties in data collection and manual annotation. These challeng
Externí odkaz:
https://doaj.org/article/59c70df38d084a1cbd954baf8c4302f9
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-20 (2024)
Abstract This work addresses a critical issue: the deterioration of concrete structures due to fine-grained cracks, which compromises their strength and longevity. To tackle this problem, experts have turned to computer vision (CV) based automated st
Externí odkaz:
https://doaj.org/article/6aed467d02c441e3b6389b7eca784c45
Publikováno v:
IEEE Access, Vol 12, Pp 64103-64113 (2024)
In recent years, Graph Convolutional Networks (GCNs) have emerged as a crucial methodology for handling graph-structured data, exhibiting superior performance in semi-supervised classification tasks. However, most existing GCNs encounter two main iss
Externí odkaz:
https://doaj.org/article/fe8332d7e66f4de484bd6a63159bd9eb
Publikováno v:
IEEE Access, Vol 12, Pp 36990-36999 (2024)
In this paper, we investigate the use of the spontaneous speech of dysarthric people for training an automatic speech recognition (ASR) model for them. Although the spontaneous speech of dysarthric people can be collected relatively easily compared t
Externí odkaz:
https://doaj.org/article/51e225758a6e43888c296ceb14e6dc71
Publikováno v:
Mathematical Biosciences and Engineering, Vol 21, Iss 2, Pp 2212-2232 (2024)
Semi-supervised medical image segmentation is currently a highly researched area. Pseudo-label learning is a traditional semi-supervised learning method aimed at acquiring additional knowledge by generating pseudo-labels for unlabeled data. However,
Externí odkaz:
https://doaj.org/article/d4e7bc5bfeae422d90083e005c447b7c
Publikováno v:
IEEE Access, Vol 12, Pp 22866-22879 (2024)
Existing supervised learning-based methods performed high-resolution visual correspondence using a decoder module. However, in self-supervised learning-based methods, it is difficult to use a decoder module that is easily influenced by labels. This p
Externí odkaz:
https://doaj.org/article/2246bfac28ec484f9f91f62c85909519
Publikováno v:
IEEE Access, Vol 12, Pp 21662-21672 (2024)
Semi-supervised semantic segmentation learns a model for classifying pixels into specific classes using a few labeled samples and numerous unlabeled images. The recent leading approach is consistency regularization by self-training with pseudo-labeli
Externí odkaz:
https://doaj.org/article/fc517bf55bb94965b86b695e838b1b81
Autor:
Usman Malik, Simon Bernard, Alexandre Pauchet, Clement Chatelain, Romain Picot-Clemente, Jerome Cortinovis
Publikováno v:
IEEE Access, Vol 12, Pp 15902-15916 (2024)
This study proposes a novel semi-supervised multi-label emotion classification approach for French tweets based on pseudo-labeling. Human subjectivity in emotional expression makes it difficult for a machine to learn. Therefore, it necessitates train
Externí odkaz:
https://doaj.org/article/01e2924da23b4cedb50d38dc2132ec1e
Autor:
Minwoo Jung, Dae-Young Kim
Publikováno v:
Applied Sciences, Vol 14, Iss 22, p 10371 (2024)
This study proposes an automated data-labeling model that combines a pseudo-labeling algorithm with waveform segmentation based on Long Short-Term Memory (LSTM) to effectively label time-series data in smart agriculture. This model aims to address th
Externí odkaz:
https://doaj.org/article/816f81cebd3245dd9115fcae8ac511a8
A Pseudo-Labeling Multi-Screening-Based Semi-Supervised Learning Method for Few-Shot Fault Diagnosis
Publikováno v:
Sensors, Vol 24, Iss 21, p 6907 (2024)
In few-shot fault diagnosis tasks in which the effective label samples are scarce, the existing semi-supervised learning (SSL)-based methods have obtained impressive results. However, in industry, some low-quality label samples are hidden in the coll
Externí odkaz:
https://doaj.org/article/d71bffc46c56473daeee44dc38d6470f