Research Progress of Few-Shot Learning Methods Based on Graph Neural Networks

Yang Jieyi; Dong Yihong; Qian Jiangbo

doi:10.7544/issn1000-1239.202220933

Journal of Computer Research and Development > 2024 > 61(4): 856-876. > DOI: 10.7544/issn1000-1239.202220933

Yang Jieyi, Dong Yihong, Qian Jiangbo. Research Progress of Few-Shot Learning Methods Based on Graph Neural Networks[J]. Journal of Computer Research and Development, 2024, 61(4): 856-876. DOI: 10.7544/issn1000-1239.202220933

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Research Progress of Few-Shot Learning Methods Based on Graph Neural Networks

1.
Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, Zhejiang 315211
2.
Key Laboratory of Mobile Network Application Technology of Zhejiang province（Ningbo University）, Ningbo, Zhejiang 315211

Funds: This work was supported by the National Natural Science Foundation of China (62271274), the Natural Science Foundation of Ningbo (2023J114), and the public welfare Technology Research project of Ningbo (2023S023).

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Author Bio:
Yang Jieyi: born in 1999. Master candidate. Student member of CCF. Her main research interests include few-shot learning, graph neural network, and machine learning

Dong Yihong: born in 1969. PhD, professor, master supervisor. Member of CCF. His main research interests include big data, data mining, and artificial intelligence

Qian Jiangbo: born in 1974. PhD, professor, PhD supervisor. Senior member of CCF. His main research interests include machine learning, pattern recognition, and intelligent systems
Received Date: November 10, 2022
Revised Date: May 15, 2023
Available Online: November 13, 2023

Graphical Abstract

Abstract

Abstract

Few-shot learning (FSL) aims to learn to get a problem-solving model using a small number of samples. Under the trend of training models with big data, deep learning has gained success in many fields, but realistic scenarios often lack sufficient samples or labeled samples. Therefore, FSL becomes a promising research direction at present. Graph neural networks (GNN) have attracted great attention due to their excellent performance in many applications. In view of this, many methods try to use GNN for FSL. Currently there are few review researches related to FSL methods based on GNN, and there is a lack of division system and introductory work on this type of methods. We systematically compose the current work related to FSL based on GNN. The work outlines the basis and concepts of graph methods for FSL, broadly classifies them into four categories of methods based on node-based feature, edge-based feature, node-pair-based feature and class-level-based feature according to the basic ideas of the models. The research progress of the four methods is introduced as well. Then the experimental results of the commonly used few-shot datasets and representative models on these datasets are summarized, as well as the advantages and disadvantages of each type of methods. Finally, current status and challenges of the graph methods for FSL are introduced, and their future directions are prospected.
- few-shot learning,
- graph neural network,
- meta-learning,
- metric-learning,
- transfer-learning

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References (103)

References

[1]	葛轶洲,刘恒,王言,等. 小样本困境下的深度学习图像识别综述[J]. 软件学报,2022,33(1):193−210 doi: 10.13328/j.cnki.jos.006342 Ge Yizhou, Liu Heng, Wang Yan, et al. Survey on deep learning image recognition in dilemma of small samples[J]. Journal of Software, 2022, 33(1): 193−210 (in Chinese) doi: 10.13328/j.cnki.jos.006342
[2]	李凡长,刘洋,吴鹏翔,等. 元学习研究综述[J]. 计算机学报,2021,44(2):422−446 doi: 10.11897/SP.J.1016.2021.00422 Li Fanchang, Liu Yang, Wu Pengxiang, et al. A survey on recent advance in meta-learning[J]. Chinese Journal of Computers, 2021, 44(2): 422−446 (in Chinese) doi: 10.11897/SP.J.1016.2021.00422
[3]	Sun Qianru, Liu Yaoyao, Chua T, et al. Meta-transfer learning for few-shot learning [C] //Proc of the 32nd IEEE Conf on Computer Vision and Pattern Recognition. Piscataway, NJ: IEEE, 2019: 403−412
[4]	Wang Peng, Liu Lingqiao, Shen Chunhua, et al. Multi-attention network for one shot learning [C] //Proc of the 30th IEEE Conf on Computer Vision and Pattern Recognition. Piscataway, NJ: IEEE, 2017: 2721−2729
[5]	Khodadadeh S, Boloni L, Shan M. Unsupervised meta-learning for few-shot image classification [C] //Proc of the 33rd Annual Conf on Neural Information-Processing Systems. Cambrige, MA: MIT, 2019: 10132−10142
[6]	赵凯琳,靳小龙,王元卓. 小样本学习研究综述[J]. 软件学报,2021,32(2):349−369 doi: 10.13328/j.cnki.jos.006138 Zhao Kailin, Jin Xiaolong, Wang Yuanzhuo. Survey on few-shot learning[J]. Journal of Software, 2021, 32(2): 349−369 (in Chinese) doi: 10.13328/j.cnki.jos.006138
[7]	Howard J, Ruder S. Universal language model fine-tuning for text classification[J]. arXiv preprint, arXiv: 1801. 06146, 2018
[8]	Royle J, Dorazio R, Link W. Analysis of multinomial models with unknown index using data augmentation[J]. Journal of Computational and Graphical Statistics, 2007, 16(1): 67−85 doi: 10.1198/106186007X181425
[9]	Liu Zicheng, Li Siyuan, Wu Di, et al. AutoMix: Unveiling the power of mixup for stronger classifiers [C] //Proc of the 17th European Conf on Computer Vision. Berlin: Springer, 2022: 441−458
[10]	Kipf T, Welling M. Semi-supervised classification with graph convolutional networks[J]. arXiv preprint, arXiv: 1609. 02907, 2016
[11]	Blaes S, Burwick T. Few-shot learning in deep networks through global prototyping[J]. Neural Networks, 2017, 94: 159−172 doi: 10.1016/j.neunet.2017.07.001
[12]	Rahman S, Khan S, Porikli F. A unified approach for conventional zero-shot, generalized zero-shot, and few-shot learning[J]. IEEE Transactions on Image Processing, 2018, 27((11): ): 5652−5267 doi: 10.1109/TIP.2018.2861573
[13]	Cheng Yu, Yu Mo, Guo Xiaoxiao, et al. Few-shot learning with meta metric learners[J]. arXiv preprint, arXiv: 1901. 09890, 2019
[14]	马帅,刘建伟,左信. 图神经网络综述[J]. 计算机研究与发展,2022,59(1):47−80 Ma Shuai, Liu Jianwei, Zuo Xin. Survey of graph neural networks[J]. Journal of Computer Research and Development, 2022, 59(1): 47−80 (in Chinese)
[15]	谢小杰,梁英,王梓森,等. 基于图卷积的异质网络节点分类方法[J]. 计算机研究与发展,2022,59(7):1470−1485 Xie Xiaojie, Liang Ying, Wang Zisen, et al. Node classification method for heterogeneous networks based on graph convolution[J]. Journal of Computer Research and Development, 2022, 59(7): 1470−1485 (in Chinese)
[16]	任嘉睿,张海燕,朱梦涵,等. 基于元图卷积的异质网络嵌入学习算法[J]. 计算机研究与发展,2022,59(8):1683−1693 Ren Jiarui, Zhang Haiyan, Zhu Menghan, et al. Embedding learning algorithm for heterogeneous networks based on metagram convolution[J]. Journal of Computer Research and Development, 2022, 59(8): 1683−1693 (in Chinese)
[17]	Xu Shiyao, Xiang Yang. Frog-GNN: Multi-perspective aggregation based graph neural network for few-shot text classification[J]. Expert Systems with Applications, 2021, 176: 114795 doi: 10.1016/j.eswa.2021.114795
[18]	Lyu Chen, Liu Weijie, Wang Ping. Few-shot text classification with edge-labeling graph neural network-based prototypical network [C] //Proc of the 28th Int Conf on Computational Linguistics. Stroudsburg, PA: ACL, 2020: 5547−552
[19]	Ding Ling, Chen Xiaojun, Xiang Yang. Negative-supervised capsule graph neural network for few-shot text classification[J]. Journal of Intelligent & Fuzzy Systems, 2021, 41(6): 6875−6887
[20]	Zhang Yuxiang, Li Wei, Zhang Mengmeng, et al. Dual graph cross-domain few-shot learning for hyperspectral image classification [C] //Proc of the 47th IEEE Int Conf on Acoustics Speech and Signal Processing. Piscataway, NJ: IEEE, 2022: 3573−3577
[21]	Song Xiaofa, Mao Mingyi, Qian Xiaohua. Auto-metric graph neural network based on a meta-learning strategy for the diagnosis of Alzheimer’s disease[J]. IEEE Journal of Biomedical and Health Informatics, 2021, 25(8): 3141−3152 doi: 10.1109/JBHI.2021.3053568
[22]	Xiong chao, Li wen, Wang Minghui, et al. Multi-dimensional edge features graph neural network on few-shot image classification[J]. IEEE Signal Processing Letters, 2021, 28: 573−577 doi: 10.1016/j.ymeth.2021.10.005
[23]	刘颖,雷研博,范九伦,等. 基于小样本学习的图像分类技术综述[J]. 自动化学报,2021,47(2):297−315 doi: 10.16383/j.aas.c190720 Liu Ying, Lei Yanbo, Fan Jiulun, et al. Survey on image classification technology based on small sample learning[J]. Acta Automatica Sinica, 2021, 47(2): 297−315 (in Chinese) doi: 10.16383/j.aas.c190720
[24]	徐冰冰,岑科廷,黄俊杰,等. 图卷积神经网络综述[J]. 计算机学报,2020,43(5):755−780 Xu Bingbing, Cen Keting, Huang Junjie, et al. A survey on graph convolutional neural networks[J]. Chinese Journal of Computers, 2020, 43(5): 755−780 (in Chinese)
[25]	Nakamura A, Harada T. Revisiting fine-tuning for few-shot learning[J]. arXiv preprint, arXiv: 1910. 00216, 2019
[26]	Dhillon G S, Ghaudhari P, Ravichandran A, et al. A baseline for few-shot image classification [C/OL] //Proc of the 8th Int Conf on Learning Representation. Amsterdam: Elsevier, 2020 [2023-03-26].https://openreview.net/forum?id=rylXBkrYDS
[27]	Wang Y, Girshick R, Hebert M, et al. Low-shot learning from imaginary data [C] //Proc of the 30th IEEE Conf on Computer Vision and Pattern Recognition. Piscataway, NJ: IEEE, 2018: 7278−7286
[28]	Liu Bo, Wang Xudong, Dixit M, et al. Feature space transfer for data augmentation [C] //Proc of the 30th IEEE Conf on Computer Vision and Pattern Recognition. Piscataway, NJ: IEEE, 2018: 9090−9098
[29]	Jing Kunlei, Zhang Xinman, Yang Zhiyuan, et al. Feature augmentation learning for few-shot palmprint image recognition with unconstrained acquisition [C] //Proc of the 47th IEEE Int Conf on Acoustics Speech and Signal Processing. Piscataway, NJ: IEEE, 2022: 3323−3327
[30]	蒋留兵,周小龙,姜风伟,等. 基于改进匹配网络的单样本学习[J]. 系统工程与电子技术,2019,41(6):1210−1217 doi: 10.3969/j.issn.1001506X.2019.06.06 Jiang Liubing, Zhou Xiaolong, Jiang Fengwei, et al. One-shot learning based on improved matching network[J]. Systems Engineering and Electronics, 2019, 41(6): 1210−1217 (in Chinese) doi: 10.3969/j.issn.1001506X.2019.06.06
[31]	Snell J, Swersky K, Zemel R. Prototypical networks for few-shot learning [C] //Proc of the 31st Annual Conf on Neural InformationProcessing Systems. Cambrige, MA: MIT, 2017: 4077−4087
[32]	Li Wenbin, Xu Jinglin, Huo Jing, et al. Distribution consistency based covariance metric networks for few-shot learning [C] //Proc of the 33rd AAAI Conf on Artificial Intelligence. Palo Alto, CA: AAAI, 2019: 8642−8649
[33]	Jiang Wen, Huang Kai, Geng Jie, et al. Multi-scale metric learning for few-shot learning[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2020, 31(3): 1091−1102
[34]	Tian Yingjie, Zhao Xiaoxi, Huang Wei. Meta-learning approaches for learning-to-learn in deep learning: A survey [J]. Neurocomputing, 2022, 494: 203−223 Tian Yingjie,Zhao Xiaoxi,Huang Wei. Meta-learning approaches for learning-to-learn in deep learning:A survey [J]. Neurocomputing,2022,494:203−223
[35]	李维刚,甘平,谢璐,等. 基于样本对元学习的小样本图像分类方法[J]. 电子学报,2022,50(2):295−304 doi: 10.12263/DZXB.20210453 Li Weigang, Gan Ping, Xie Lu, et al. A few-shot image classification method by pairwise-based meta learning[J]. Acta Electronica Sinica, 2022, 50(2): 295−304 (in Chinese) doi: 10.12263/DZXB.20210453
[36]	Ravi S, Larochelle H. Optimization as a model for few-shot learning [C/OL] //Proc of the 4th Int Conf on Learning Representation. Amsterdam: Elsevier, 2016 [2023-03-26].https://openreview.net/pdf?id=rJY0-Kcll
[37]	Finn C, Abbeel P, Levine S. Model-agnostic meta-learning for fast adaptation of deep networks [C] //Proc of the 34th ACM Int Conf on Machine Learning. New York: ACM, 2017: 1126−1135
[38]	Li Zhenguo, Zhuo Fengwei, Chen Fei, et al. Meta-SGD: Learning to learn quickly for few-shot learning[J]. arXiv preprint, arXiv: 1707. 09835, 2017
[39]	Vinyals O, Blundell C, Lilicrap T, et al. Matching networks for one shot learning [C] //Proc of the 30th Annual Conf on Neural Information Processing Systems. Cambrige, MA: MIT, 2016: 3630−3638
[40]	Santoro A, Bartunov S, Botvinick M, et al. Meta-learning with memory-augmented neural networks [C] //Proc of the 33rd ACM Int Conf on Machine Learning. New York: ACM, 2016: 1842−1850
[41]	Brendel W, Bethge M. Approximating CNNs with bag-of-local-features models works surprisingly well on ImageNet [C/OL] //Proc of the 7th Int Conf on Learning Representation. Amsterdam: Elsevier, 2019 [2023-03-26].https://openreview.net/forum?id=rJY0-Kcll
[42]	Lai K, Zha Daochen, Zhou Kaixiong, et al. Policy-GNN: Aggregation optimization for graph neural networks [C] //Proc of the 26th ACM SIGKDD Conf on Knowledge Discovery and Data Mining. New York: ACM, 2020: 461−471
[43]	Garcia V, Bruna J. Few-shot learning with graph neural networks [C/OL] //Proc of the 6th Int Conf on Learning Representation. Amsterdam: Elsevier, 2018 [2023-03-26].https://openreview.net/pdf ?id=BJj6qGbRW
[44]	Liu Yanbin, Lee J, Park M, et al. Learning to propagate labels: Transductive propagation network for few-shot learning [C/OL] //Proc of the 7th Int Conf on Learning Representation. Amsterdam: Elsevier, 2019 [2023-03-26].https://openreview.net/pdf?id=SyVuRiC5K7
[45]	Qiao Limeng, Shi Yemin, Li Jia, et al. Transductive episodic-wise adaptive metric for few-shot learning [C] //Proc of the 11th IEEE Int Conf on Computer Vision. Piscataway, NJ: IEEE, 2019: 3602−3611
[46]	Angluin D, Smith C. Inductive inference: Theory and methods[J]. ACM Computing Surveys, 1983, 15(3): 237−269 doi: 10.1145/356914.356918
[47]	Chen Jinyin, Lin Xiang, Xiong Hui, et al. Smoothing adversarial training for GNN[J]. IEEE Transactions on Computational Social Systems, 2020, 8(3): 618−629
[48]	Cheng Hao, Zhou J, Tay W, et al. Attentive graph neural networks for few-shot learning [C] //Proc of the 5th IEEE Int Conf on Multimedia Information Processing and Retrieval. Piscataway, NJ: IEEE, 2022: 152−157
[49]	Velickovic P, Cucurull G, Casanova A, et al. Graph attention networks[J]. arXiv preprint, arXiv: 1710. 10903, 2017
[50]	Zhang Xu, Zhang Youjia, Zhang Zuyu, et al. Discriminative learning of imaginary data for few-shot classification [J]. Neurocomputing, 2022, 467: 406−417 Zhang Xu,Zhang Youjia,Zhang Zuyu,et al. Discriminative learning of imaginary data for few-shot classification [J]. Neurocomputing,2022,467:406−417
[51]	Xu Rui, Liu Baodi, Lu Xiaoping, et al. DMH-FSL: Dual-modal hypergraph for few-shot learning[J]. Neural Processing Letters, 2022, 54(2): 1317−1332 doi: 10.1007/s11063-021-10684-7
[52]	Yadati N, Nimishakavi M, Yadav P, et al. HyperGCN: A new method for training graph convolutional networks on hypergraphs [C] //Proc of the 33rd Annual Conf on Neural Information Processing Systems. Cambrige, MA: MIT, 2019: 1509−1520
[53]	Kim J, Kim T, Kim S, et al. Edge-labeling graph neural network for few-shot learning [C] //Proc of the 32nd IEEE Conf on Computer Vision and Pattern Recognition. Piscataway, NJ: IEEE, 2019: 11−20
[54]	Zuo Xibing, Yu Xuchun, Liu Bing, et al. FSL-EGNN: Edge-labeling graph neural network for hyperspectral image few-shot classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 1−18
[55]	Xiong Chao, Li Wen, Liu Yun, et al. Multi-dimensional edge features graph neural network on few-shot image classification[J]. IEEE Signal Processing Letters, 2021, 28: 573−577 doi: 10.1109/LSP.2021.3061978
[56]	Tang Shixiang, Chen Dapeng, Bai Lei, et al. Mutual CRF-GNN for few-shot learning [C] //Proc of the 34th IEEE Conf on Computer Vision and Pattern Recognition. Piscataway, NJ: IEEE, 2021: 2329−2339
[57]	Bale T, Vale W. CRF and CRF receptors: Role in stress responsivity and other behaviors [J]. Annual Review of Pharmacology and Toxicology, 2004, 44: 525−557 Bale T,Vale W. CRF and CRF receptors:Role in stress responsivity and other behaviors [J]. Annual Review of Pharmacology and Toxicology,2004,44:525−557
[58]	Yang Lin, Li Liangliang, Zhang Zilun, et al. DPGNN: Distribution propagation graph network for few-shot learning [C] //Proc of the 33rd IEEE Conf on Computer Vision and Pattern Recognition. Piscataway, NJ: IEEE, 2020: 13387−13396
[59]	Zhang Bailin, Ling Hefei, Shen Jialie, et al. Mixture distribution graph network for few shot learning[J]. IEEE Transactions on Cognitive and Developmental Systems, 2022, 14(3): 892−901 doi: 10.1109/TCDS.2021.3075280
[60]	Hu Yufan, Gao Junyu, Xu Changsheng. Learning dual-pooling graph neural networks for few-shot video classification[J]. IEEE Transactions on Multimedia, 2020, 23: 4285−4296
[61]	Mordeson J. Fuzzy mathematics [M]. Foundations of Image Understanding. Berlin: Springer, 2001: 95−125
[62]	Wei Tong, Hou Junlin, Feng Rui. Fuzzy graph neural network for few-shot learning [C/OL] //Proc of the 30th IEEE Int Joint Conf on Neural Networks. Piscataway, NJ: IEEE, 2020 [2023-03-26].https://openreview.net/pdf?id=BJj6qGbRW
[63]	Devlin J, Chang M, Lee K, et al. Bert: Pre-training of deep bidirectional transformers for language understanding [C] //Proc of the 17th Annual Conf of the North American chapter of the Association for Computational Linguistics. Stroudsburg, PA: ACL, 2019: 4171−4186
[64]	Ma Yuqing, Bai Shihao, An Shan, et al. Transductive relation-propagation network for few-shot Learning [C] //Proc of the 29th Int Joint Conf on Artificial Intelligence. San Francisco, CA: Morgan Kaufmann, 2020: 804−810
[65]	Ma Yuqing, Bai Shihao, Liu Wei, et al. Transductive relation-propagation with decoupling training for few-shot learning[J]. IEEE Transactions on Neural Networks and Learning Systems, 2022, 33(11): 6652−6664 doi: 10.1109/TNNLS.2021.3082928
[66]	Chen Cen, Li Kenli, Wei Wei, et al. Hierarchical graph neural networks for few-shot learning[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2021, 32(1): 240−252
[67]	Chen Chaofan, Yang Xiaoshan, Xu Changsheng, et al. ECKPN: Explicit class knowledge propagation network for transductive few-shot learning [C] //Proc of the 34th IEEE Conf on Computer Vision and Pattern Recognition. Piscataway, NJ: IEEE, 2021: 6596−6605
[68]	Pennington J, Socher R, Manning C. Glove: Global vectors for word representation [C] //Proc of the 28th Int Conf on Empirical Methods in Natural Language Processing. Stroudsburg, PA: ACL, 2014: 1532−1543
[69]	Yu Tianyuan, He Sen, Song Yizhe, et al. Hybrid graph neural networks for few-shot learning [C] //Proc of the 36th AAAI Conf on Artificial Intelligence. Palo Alto, CA: AAAI, 2022: 3179−3187
[70]	Deng Fei, Pu Shengliang, Chen Xuehong, et al. Hyperspectral image classification with capsule network using limited training samples[J]. Sensors, 2018, 18(9): 3153−3174 doi: 10.3390/s18093153
[71]	Zhang Ruiheng, Yang Shuo, Zhang Qi, et al. Graph-based few-shot learning with transformed feature propagation and optimal class allocation[J]. Neurocomputing, 2022, 470: 247−256 doi: 10.1016/j.neucom.2021.10.110
[72]	Tantawi A, Towsley G, Wolf J. Optimal allocation of multiple class resources in computer systems [C] //Proc of the 16th ACM SIGMETRICS Conf on Measurement and Modeling of Computer Systems. New York: ACM, 1988: 253−260
[73]	Cuturi M. Sinkhorn distances: Lightspeed computation of optimal transport [C] //Proc of the 27th Annual Conf on Neural Information Processing Systems. Cambrige, MA: MIT, 2013: 2292−2300
[74]	Malalur P, Jaakkola T. Alignment based matching networks for one-shot classification and open-set recognition[J]. arXiv preprint, arXiv: 1903. 06538, 2019
[75]	Ren M, Triantafillou E, Ravi S, et al. Meta-learning for semi-supervised few-shot classification [C/OL]//Proc of the 6th Int Conf on Learning Representation. Amsterdam: Elsevier, 2018 [2023-03-26].https://openreview.net/pdf?id=HJcSzz-CZ
[76]	Cui Yin , Zhou Feng, Lin Yuanqing, et al. Fine-grained categorization and dataset bootstrapping using deep metric learning with humans in the loop [C] //Proc of the 29th IEEE Conf on Computer Vision and Pattern Recognition. Piscataway, NJ: IEEE, 2016: 1153−1162
[77]	Singla S, Singla S, Feizi S. Improved deterministic l₂ robustness on CIFAR-10 and CIFAR-100 [C/OL] //Proc of the 10th Int Conf on Learning Representation. Amsterdam: Elsevier, 2022 [2023-03-26].https://openreview.net/forum?id=tD7eCtaSkR
[78]	Deng J, Dong W, Socher R, et al. ImageNet: A large-scale hierarchical image database [C] //Proc of the 22nd IEEE Conf on Computer Vision and Pattern Recognition. Piscataway, NJ: IEEE, 2009: 248−255
[79]	He Kaiming, Zhang Xianyu, Ren Shaoqing, et al. Deep residual learning for image recognition [C] //Proc of the 29th IEEE Conf on Computer Vision and Pattern Recognition. Piscataway, NJ: IEEE, 2016: 770−778
[80]	Lin Guangfeng, Yang Ying, Fan Yindi, et al. High-order structure preserving graph neural network for few-shot learning[J]. arXiv preprint, arXiv: 2005. 14415, 2020
[81]	Wang Yaqing, Yao Quanming, Kwok J, et al. Generalizing from a few examples: A survey on few-shot learning[J]. ACM Computing Surveys, 2020, 53(3): 1−34
[82]	Bendre N, Marin H, Najafirad P. Learning from few samples: A survey[J]. arXiv preprint, arXiv: 2007. 15484, 2020
[83]	Li Na, Zhou Deyun, Shi Jiao, et al. Graph-based deep multitask few-shot learning for hyperspectral image classification[J]. Remote Sensing, 2022, 14(9): 2246−2267 doi: 10.3390/rs14092246
[84]	Tong Xinyi, Yin Jihao, Han Bingnan, et al. Few-shot learning with attention-weighted graph convolutional networks for hyperspectral image classification [C] //Proc of the 27th IEEE Int Conf on Image Processing. Piscataway, NJ: IEEE, 2020: 1686−1690
[85]	Hong Danfeng, Gao Lianru, Yao Jing, et al. Graph convolutional networks for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(7): 5966−5978 doi: 10.1109/TGRS.2020.3015157
[86]	Xiao Weiwei, Song Kechen, Liu Jie, et al. Graph embedding and optimal transport for few-shot classification of metal surface defect[J]. IEEE Transactions on Instrumentation and Measurement, 2022, 71: 1−10
[87]	Bao Yanqi, Song Kechen, Liu Jie, et al. Triplet-graph reasoning network for few-shot metal generic surface defect segmentation[J]. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 1−11
[88]	Li Feimo, Li Shuaibo, Fan Xinxin, et al. Structural attention enhanced continual meta-learning for graph edge labeling based few-shot remote sensing scene classification[J]. Remote Sensing, 2022, 14(3): 485−515 doi: 10.3390/rs14030485
[89]	Yuan Zhengwu, Huang Wendong, Tang Chan, et al. Graph-based embedding smoothing network for few-shot scene classification of remote sensing images[J]. Remote Sensing, 2022, 14(5): 1161−1179 doi: 10.3390/rs14051161
[90]	Guo Xinyu, Tian Bingjie, Tian Xuedong. HFGNN-proto: Hesitant fuzzy graph neural network-based prototypical network for few-shot text classification[J]. Electronics, 2022, 11(15): 2423−2437 doi: 10.3390/electronics11152423
[91]	Ma Ning, Bu Jiajun, Yang Jieyu, et al. Adaptive-step graph meta-learner for few-shot graph classification [C] //Proc of the 29th ACM Int Conf on Information & Knowledge Management. New York: ACM, 2020: 1055−1064
[92]	Hospedales T, Antoniou A, Micaelli P, et al. Meta-learning in neural networks: A survey[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 44(9): 5149−5169
[93]	Pan Jiacheng, Lin Haocai, Dong Yihong, et al. MAMF-GCN: Multi-scale adaptive multi-channel fusion deep graph convolutional network for predicting mental disorder[J]. Computers in Biology and Medicine, 2022, 148: 105823 doi: 10.1016/j.compbiomed.2022.105823
[94]	Wei Yinwei, Wang Xiang, Nie Liqiang, et al. MMGCN: Multi-modal graph convolution network for personalized recommendation of micro-video [C] //Proc of the 27th ACM Int Conf on Multimedia. New York: ACM, 2019: 1437−144
[95]	Tao Zhulin, Wei Yinwei, Wang Xiang, et al. MGAT: Multimodal graph attention network for recommendation[J]. Information Processing & Management, 2020, 57(5): 102277
[96]	Hsu K, Levine S, Finn C. Unsupervised learning via meta-learning [C/OL] //Proc of the 7th Int Conf on Learning Representation. Amsterdam: Elsevier, 2018 [2023-03-26].https://openreview.net/forum?id=r1My6sR9tX
[97]	Antoniou A, Storkey A. Assume, augment and learn: Unsupervised few-shot meta-learning via random labels and data augmentation[J]. arXiv preprint, arXiv: 1902. 09884, 2019
[98]	Veeriah V, Hessel M, Xu Zhongwen, et al. Discovery of useful questions as auxiliary tasks [C] // Proc of the 33rd Annual Conf on Neural Information Processing Systems. Cambrige, MA: MIT, 2019: 9306−9317
[99]	Zheng Zeyu, Oh J, Singh S. On learning intrinsic rewards for policy gradient methods [C] // Proc of the 32nd Annual Conf on Neural Information Processing Systems. Cambrige, MA: MIT, 2018: 4649−4659
[100]	Meier F, Kappler D, Schaal S. Online learning of a memory for learning rates [C] //Proc of the 4th IEEE Int Conf on Robotics and Automation. Piscataway, NJ: IEEE, 2018: 2425−2432
[101]	Tseng H, Lee H, Huang Jiabin, et al. Cross-domain few-shot classification via learned feature-wise transformation [C/OL] //Proc of the 9th Int Conf on Learning Representation. Amsterdam: Elsevier, 2020 [2023-03-26].https://openreview.net/forum?id=SJl5Np4tPr
[102]	Yun S, Jeong M, Kim R, et al. Graph transformer networks [C] // Proc of the 33rd Annual Conf on Neural Information Processing Systems. Cambrige, MA: MIT, 2019: 11960−11970
[103]	Jiang Bo, Zhao Kangkang, Tang jin, et al. RGTransformer: Region-graph transformer for image representation and few-shot classification[J]. IEEE Signal Processing Letters, 2022, 29: 792−796 doi: 10.1109/LSP.2022.3155991

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