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Dual Relation Semi-Supervised Multi-Label Learning

Lichen Wang, Yunyu Liu, Can Qin, Gan Sun, Yun Fu
2020 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
To this end, we proposed a Dual Relation Semi-supervised Multi-label Learning (DRML) approach which jointly explores the feature distribution and the label relation simultaneously.  ...  Multi-label learning (MLL) solves the problem that one single sample corresponds to multiple labels.  ...  Conclusion In this paper, we proposed a Dual Relation Multi-label Learning (DRML) approach for Semi-supervised manner.  ... 
doi:10.1609/aaai.v34i04.6089 fatcat:kn3rlarcyfekxnfttiggqrfr3i

Learning from partially labeled data

Siamak Mehrkanoon, Xiaolin Huang, Johan A. K. Suykens
2020 The European Symposium on Artificial Neural Networks  
In particular, in this context one can refer to semi-supervised modelling, transfer learning, domain adaptation and multi-view learning among others.  ...  Designing models that can learn from partially labeled data, or leveraging labeled data in one domain and unlabeled data in a different but related domain is of great interest in many applications.  ...  The semi-supervised learning, domain adaption, multi-view learning are among existing proposed models. Semi-supervised models use both labeled and unlabeled data points in the learning process.  ... 
dblp:conf/esann/MehrkanoonHS20 fatcat:hdjcnwwu4fgzbjwv5uotkcyvua

Multi-Modal Hypergraph Diffusion Network with Dual Prior for Alzheimer Classification [article]

Angelica I. Aviles-Rivero, Christina Runkel, Nicolas Papadakis, Zoe Kourtzi, Carola-Bibiane Schönlieb
2022 arXiv   pre-print
In this work, we introduce a novel semi-supervised hypergraph learning framework for Alzheimer's disease diagnosis.  ...  Our framework allows for higher-order relations among multi-modal imaging and non-imaging data whilst requiring a tiny labelled set.  ...  Our contributions are as follows. 1) We introduce a self-supervised dual multi-modal embedding strategy.  ... 
arXiv:2204.02399v2 fatcat:roc3is2pz5fbnbmo6q7copmzia

Multi-modal Sentiment Classification with Independent and Interactive Knowledge via Semi-supervised Learning

Dong Zhang, Shoushan Li, Qiaoming Zhu, Guodong Zhou
2020 IEEE Access  
In this paper, we aim to reduce the annotation effort for multi-modal sentiment classification via semi-supervised learning.  ...  Empirical evaluation demonstrates the great effectiveness of the proposed semi-supervised approach to multi-modal sentiment classification.  ...  In comparison, semi-supervised sentiment classification has much less related studies. Li et al.  ... 
doi:10.1109/access.2020.2969205 fatcat:bkcydvpc7bgs3l6n4usfz7lvcy

Dual-Teacher: Integrating Intra-domain and Inter-domain Teachers for Annotation-efficient Cardiac Segmentation [article]

Kang Li, Shujun Wang, Lequan Yu, Pheng-Ann Heng
2020 arXiv   pre-print
To this end, we propose a novel semi-supervised domain adaptation approach, namely Dual-Teacher, where the student model not only learns from labeled target data (e.g., CT), but also explores unlabeled  ...  ,semi-supervised learning further exploring plentiful unlabeled data, domain adaptation including multi-modality learning and unsupervised domain adaptation resorting to the prior knowledge from additional  ...  By simultaneously exploiting all of data resources, our Dual-Teacher outperforms the unsupervised domain adaptation, multi-modality learning and semi-supervised learning methods by a large margin, i.e.  ... 
arXiv:2007.06279v1 fatcat:rtungnerpvhs5bav2tkymvvdsq

A New SSOPMV Learning for Matrix Data Sets

Changming Zhu, Chengjiu Mei, Rigui Zhou, Lai Wei, Xiafen Zhang, Min Yao
2018 IOP Conference Series: Materials Science and Engineering  
Related experiments validate that MSSOPMV can process multi-view, semi-supervised, large-scale, and matrix data sets well.  ...  In order to process these data sets, scholars have developed semi-supervise done-pass multi-view learning (SSOPMV). While SSOPMV cannot process matrix data sets.  ...  learning machines to process the related semi-supervised data sets.  ... 
doi:10.1088/1757-899x/466/1/012111 fatcat:7sijfw2f5fg5poc2xpvrr7xciy

Dual-Consistency Semi-Supervised Learning with Uncertainty Quantification for COVID-19 Lesion Segmentation from CT Images [article]

Yanwen Li, Luyang Luo, Huangjing Lin, Hao Chen, Pheng-Ann Heng
2021 arXiv   pre-print
To tackle the challenge of limited annotations, in this paper, we propose an uncertainty-guided dual-consistency learning network (UDC-Net) for semi-supervised COVID-19 lesion segmentation from CT images  ...  Extensive experiments showed that our proposed UDC-Net improves the fully supervised method by 6.3% in Dice and outperforms other competitive semi-supervised approaches by significant margins, demonstrating  ...  Wang, X., Chen, H., Ran, A.R., Luo, L., Chan, P.P., Tham, C.C., et al.: Towards multi-center glaucoma oct image screening with semi-supervised joint structure and function multi-task learning.  ... 
arXiv:2104.03225v2 fatcat:shaahzvnafgo5ivx5wptcvcste

Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation [article]

Rushi Jiao, Yichi Zhang, Le Ding, Rong Cai, Jicong Zhang
2022 arXiv   pre-print
Semi-supervised learning has emerged as an appealing strategy and been widely applied to medical image segmentation tasks to train deep models with limited annotations.  ...  In this paper, we present a comprehensive review of recently proposed semi-supervised learning methods for medical image segmentation and summarized both the technical novelties and empirical results.  ...  the training data through label generation [19] , data augmentation [20] , leveraging external related labeled datasets [21] , and leveraging unlabeled data with semi-supervised learning.  ... 
arXiv:2207.14191v2 fatcat:j3o3vg5vd5dsxg5i3y6ovxlrey

Tractable Semi-supervised Learning of Complex Structured Prediction Models [chapter]

Kai-Wei Chang, S. Sundararajan, S. Sathiya Keerthi
2013 Lecture Notes in Computer Science  
Semi-supervised learning has been widely studied in the literature.  ...  In this paper, we propose an approximate semi-supervised learning method that uses piecewise training for estimating the model weights and a dual decomposition approach for solving the inference problem  ...  We review the related work in Section 2. In Section 3 we provide a background on semi-supervised learning, label assignment problem, and composite likelihood.  ... 
doi:10.1007/978-3-642-40994-3_12 fatcat:i5oud3x74rav5hnxnj7kr4wirm

Parameterized Semi-supervised Classification Based on Support Vector for Multi-relational Data [chapter]

Ling Ping, Zhou Chun-Guang
2006 Lecture Notes in Computer Science  
A Parameterized Semi-supervised Classification algorithm based on Support Vector (PSCSV) for multi-relational data is presented in this paper.  ...  Data is labeled according to its affinity to class centers. A novel Kernel function encoded in PSCSV is defined for multi-relational version and parameterized by supervisory information.  ...  With this weak label information or sideinformation, classification issue is equivalent to semi-supervised clustering where the decision model is learned from all data.  ... 
doi:10.1007/11881070_10 fatcat:ii2x2bybxjavrjukssqljun7fi

Duo-SegNet: Adversarial Dual-Views for Semi-Supervised Medical Image Segmentation [article]

Himashi Peiris, Zhaolin Chen, Gary Egan, Mehrtash Harandi
2021 arXiv   pre-print
To address this issue, we propose a semi-supervised image segmentation technique based on the concept of multi-view learning.  ...  In contrast to the previous art, we introduce an adversarial form of dual-view training and employ a critic to formulate the learning problem in multi-view training as a min-max problem.  ...  We propose a dual-view learning scheme for semi-supervised medical image segmentation. 2.  ... 
arXiv:2108.11154v1 fatcat:u2qmon2xbjaapadczm4r6e5h3e

KeCo: Kernel-Based Online Co-agreement Algorithm [chapter]

Laurens Wiel, Tom Heskes, Evgeni Levin
2015 Lecture Notes in Computer Science  
We propose a kernel-based online semi-supervised algorithm that is applicable for large scale learning tasks.  ...  In particular, we use a multi-view learning framework and a co-agreement strategy to take into account unlabelled data and to improve classification performance of the algorithm.  ...  Recently an online multi-view algorithm has been proposed in [9] . The algorithm can operate in the semi-supervised regime by using co-regularization.  ... 
doi:10.1007/978-3-319-24282-8_26 fatcat:4tchsmtyere2tkdwxvdr4mhzyq

Semi-supervised Medical Image Segmentation through Dual-task Consistency [article]

Xiangde Luo, Jieneng Chen, Tao Song, Guotai Wang
2021 arXiv   pre-print
To answer this question, we propose a novel dual-task-consistency semi-supervised framework for the first time.  ...  Deep learning-based semi-supervised learning (SSL) algorithms have led to promising results in medical images segmentation and can alleviate doctors' expensive annotations by leveraging unlabeled data.  ...  Related Works Semi-Supervised Medical Image Segmentation: For semi-supervised medical image segmentation, traditional methods mainly use hand-crafted features to design a model to perform segmentation,  ... 
arXiv:2009.04448v2 fatcat:x7bsdzugcvfdjbyrb4uqxyobr4

Turbo Learning for Captionbot and Drawingbot [article]

Qiuyuan Huang, Pengchuan Zhang, Dapeng Wu, Lei Zhang
2018 arXiv   pre-print
Furthermore, the turbo-learning approach enables semi-supervised learning since the closed loop can provide pseudo-labels for unlabeled samples.  ...  The key idea behind the joint training is that image-to-text generation and text-to-image generation as dual problems can form a closed loop to provide informative feedback to each other.  ...  for supervised and semi-supervised learning.  ... 
arXiv:1805.08170v2 fatcat:dc3xedekljhrha6r3f3lfaciqa

Semi-Supervised Learning via Generalized Maximum Entropy

Ayse Erkan, Yasemin Altun
2010 Journal of machine learning research  
We extend this framework to semi-supervised learning by incorporating unlabeled data via modifications to these potential functions reflecting structural assumptions on the data geometry.  ...  The proposed approach leads to a family of discriminative semi-supervised algorithms, that are convex, scalable, inherently multi-class, easy to implement, and that can be kernelized naturally.  ...  Investigating the difference between the dual supervised and semi-supervised formulations, ( 4 ) and ( 8 ), we observe that D * x term is evaluated on both labeled and unlabeled data in the semi-supervised  ... 
dblp:journals/jmlr/ErkanA10 fatcat:mvyjyswbmvcs3hmivxb5hysmfa
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