CReST: A Class-Rebalancing Self-Training Framework for Imbalanced Semi-Supervised Learning [PDF]
Computer Vision and Pattern Recognition, 2021 Semi-supervised learning on class-imbalanced data, although a realistic problem, has been under studied. While existing semi-supervised learning (SSL) methods are known to perform poorly on minority classes, we find that they still generate high ...
Chen Wei +4 more
semanticscholar +1 more source
Classification Uncertainty Minimization-based Semi-supervised Ensemble Learning Algorithm [PDF]
Jisuanji kexue, 2023 Semi-supervised ensemble learning(SSEL) is a combinatorial paradigm by fusing semi-supervised learning and ensemble learning together,which improves the diversity of ensemble learning by introducing unlabeled samples and at the same time solves the ...
HE Yulin, ZHU Penghui, HUANG Zhexue, Fournier-Viger PHILIPPE
doaj +1 more source
Semi-supervised Learning on Graphs Using Adversarial Training with Generated Sample [PDF]
Jisuanji kexue yu tansuo, 2023 Given a graph composed of a small number of labeled nodes and a large number of unlabeled nodes, semi-supervised learning on graphs aims to assign labels for the unlabeled nodes.
WANG Cong, WANG Jie, LIU Quanming, LIANG Jiye
doaj +1 more source
OBJECT DETECTION USING SEMI SUPERVISED LEARNING METHODS
ICTACT Journal on Soft Computing, 2022 Object detection is used to identify objects in real time using some deep learning algorithms. In this work, wheat plant data set around the world is collected to study the wheat heads.
Shymala Gowri Selvaganapathy +3 more
doaj +1 more source
A Survey on Deep Semi-Supervised Learning [PDF]
IEEE Transactions on Knowledge and Data Engineering, 2021 Deep semi-supervised learning is a fast-growing field with a range of practical applications. This paper provides a comprehensive survey on both fundamentals and recent advances in deep semi-supervised learning methods from perspectives of model design ...
Xiangli Yang +3 more
semanticscholar +1 more source
An Improved Algorithm of Drift Compensation for Olfactory Sensors
Applied Sciences, 2022 This research mainly studies the semi-supervised learning algorithm of different domain data in machine olfaction, also known as sensor drift compensation algorithm.
Siyu Lu +6 more
doaj +1 more source
Semi-supervised Object Detection with Sequential Three-way Decision [PDF]
Jisuanji kexue, 2023 The need for large scale data in deep learning and the complexity of object detection annotation task promote the deve-lopment of semi-supervised object detection.In recent years,semi-supervised object detection has achieved many excellent results ...
SONG Faxing, MIAO Duoqian, ZHANG Hongyun
doaj +1 more source
Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support Samples [PDF]
IEEE International Conference on Computer Vision, 2021 This paper proposes a novel method of learning by predicting view assignments with support samples (PAWS). The method trains a model to minimize a consistency loss, which ensures that different views of the same unlabeled instance are assigned similar ...
Mahmoud Assran +6 more
semanticscholar +1 more source
On semi-supervised learning [PDF]
TEST, 2019 arXiv admin note: substantial text overlap with arXiv:1709 ...
A. Cholaquidis, R. Fraiman, M. Sued
openaire +4 more sources
Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning [PDF]
AAAI Conference on Artificial Intelligence, 2018 Many interesting problems in machine learning are being revisited with new deep learning tools. For graph-based semi-supervised learning, a recent important development is graph convolutional networks (GCNs), which nicely integrate local vertex ...
Qimai Li, Zhichao Han, Xiao-Ming Wu
semanticscholar +1 more source