[1] |
ZHU X J, GOLDBERG A B. Introduction to Semi-supervised Learning[M]. Morgan and Claypool Publishers, 2009.
|
[2] |
TARVAINEN A, VALPOLA H. Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results[C]// Proceedings of the 31st International Conference on Neural Information Processing Systems. 2017:1195-1204.〖HJ1.6mm〗
|
[3] |
BERTHELOT D, CARLINI N, GOODFELLOW I, et al. Mixmatch: A holistic approach to semi-supervised learning[C]// Proceedings of the 33rd International Conference on Neural Information Processing Systems. 2019:5049-5059.
|
[4] |
BERTHELOT D, CARLINI N, CUBUK E D, et al. Remixmatch: Semi-supervised learning with distribution alignment and augmentation anchoring[J]. arXiv preprint arXiv:1911.09785, 2019.
|
[5] |
SOHN K, BERTHELOT D, LI C L, et al. Fixmatch: Simplifying semi-supervised learning with consistency and confidence[J]. arXiv preprint arXiv:2001.07685, 2021.
|
[6] |
LI J N, XIONG C M, HOI S C H. CoMatch: Semi-supervised learning with contrastive graph regularization[C]// 2021 IEEE/CVF International Conference on Computer Vision (ICCV). 2021:9455-9464.
|
[7] |
WANG H, NIE F P, CAI W D, et al. Semi-supervised robust dictionary learning via efficient l-norms minimization[C]// Proceedings of the IEEE International Conference on Computer Vision. 2013:1145-1152.
|
[8] |
WANG D, ZHANG X Q, FAN M Y, et al. Semi-supervised dictionary learning via structural sparse preserving[C] // Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence. 2016:2137-2144.
|
[9] |
CHEN L, YU S Q, YANG M. Semi-supervised convolutional neural networks with label propagation for image classification[C]// 2018 24th International Conference on Pattern Recognition (ICPR). 2018:1319-1324.
|
[10] |
YANG M, CHEN L. Discriminative semi-supervised dictionary learning with entropy regularization for pattern classification[C]// Proceedings of the 31st AAAI Conference on Artificial Intelligence. 2017: 1626-1632.
|
[11] |
LIU Y, CHEN Q C, CHEN W, et al. Dictionary learning inspired deep network for scene recognition[C]// Proceedings of the 32nd AAAI Conference on Artificial Intelligence and 30th Innovative Applications of Artificial Intelligence Conference and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence. 2018:7178-7185.
|
[12] |
MAHDIZADEHAGHDAM S, PANAHI A, KRIM H, et al. Deep dictionary learning: A parametric network approach[J]. IEEE Transactions on Image Processing, 2019,28(10):4790-4802.
|
[13] |
TANG H, LIU H, XIAO W, et al. When dictionary learning meets deep learning: Deep dictionary learning and coding network for image recognition with limited data[J]. IEEE Transactions on Neural Networks and Learning Systems, 2020,32(5):2129-2141.
|
[14] |
孙劲光,李燕北,魏宪,等. 结合卷积神经网络和稀疏编码的高光谱图像分类[J]. 激光与光电子学进展, 2020,57(18):399-408.
|
[15] |
WEI X, SHEN H, KLEINSTEUBER M. Trace quotient with sparsity priors for learning low dimensional image representations[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019,42(12):3119-3135.
|
[16] |
KRIZHEVSKY A, SUTSKEVER I, HINTON G E. Imagenet classification with deep convolutional neural networks[J]. Advances in Neural Information Processing Systems, 2012,25:1097-1105.
|
[17] |
SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[J]. arXiv preprint arXiv:1409.1556, 2014.
|
[18] |
HE K M, ZHANG X, REN S Q, et al. Deep residual learning for image recognition[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016:770-778.
|
[19] |
ZAGORUYKO S, KOMODAKIS N. Wide residual networks[J]. arXiv preprint arXiv:1605.07146, 2016.
|
[20] |
HE K M, ZHANG X, REN S Q, et al. Identity mappings in deep residual networks[C]// European Conference on Computer Vision. Springer. 2016:630-645.
|
[21] |
XIE S N, GIRSHICK R, DOLLR P, et al. Aggregated residual transformations for deep neural networks[C]// 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2017:5987-5995.
|
[22] |
RASMUS A, VALPOLA H, HONKALA M, et al. Semi-supervised learning with Ladder networks[C]// Proceedings of the 28th International Conference on Neural Information Processing Systems. 2015:3546-3554..
|
[23] |
JIANG B, ZHANG Z Y, LIN D D, et al. Semi-supervised learning with graph learning-convolutional networks[C]// 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2019:11305-11312.
|
[24] |
KEJANI M T, DORNAIKA F, TALEBI H. Graph Convolution Networks with manifold regularization for semi-supervised learning[J].Neural Networks, 2020,127:160-167.
|
[25] |
ZOU H, HASTIE T. Regularization and variable selection via the elastic net[J]. Journal of the Royal Statistical Society (series B), 2005,67(2):301-320.
|
[26] |
KOKIOPOULOU E, CHEN J, SAAD Y. Trace optimization and eigenproblems in dimension reduction methods[J]. Numerical Linear Algebra with Applications, 2011,18(3):565-602.
|
[27] |
WANG J Y, CHEN Y B, CHAKRABORTY R, et al. Orthogonal convolutional neural networks[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2020:11505-11515.
|
[28] |
ABSIL P A, MAHONY R, SEPULCHRE R.Optimization Algorithms on Matrix Manifolds[M]. Princeton University Press, 2009.
|
[29] |
KLEINSTEUBER M, HUPER K. An intrinsic CG algorithm for computing dominant subspaces[C]// 2007 IEEE International Conference on Acoustics, Speech and Signal Processing. 2007:1405-1408.
|