@String(PAMI = {IEEE Trans. Pattern Anal. Mach. Intell.})
@String(IJCV = {Int. J. Comput. Vis.})
@String(CVPR= {IEEE Conf. Comput. Vis. Pattern Recog.})
@String(ICCV= {Int. Conf. Comput. Vis.})
@String(ECCV= {Eur. Conf. Comput. Vis.})
@String(NIPS= {Adv. Neural Inform. Process. Syst.})
@String(ICPR = {Int. Conf. Pattern Recog.})
@String(BMVC= {Brit. Mach. Vis. Conf.})
@String(TOG= {ACM Trans. Graph.})
@String(TIP  = {IEEE Trans. Image Process.})
@String(TVCG  = {IEEE Trans. Vis. Comput. Graph.})
@String(TMM  = {IEEE Trans. Multimedia})
@String(ACMMM= {ACM Int. Conf. Multimedia})
@String(ICME = {Int. Conf. Multimedia and Expo})
@String(ICASSP=	{ICASSP})
@String(ICIP = {IEEE Int. Conf. Image Process.})
@String(ACCV  = {ACCV})
@String(ICLR = {Int. Conf. Learn. Represent.})
@String(IJCAI = {IJCAI})
@String(PR   = {Pattern Recognition})
@String(AAAI = {AAAI})
@String(CVPRW= {IEEE Conf. Comput. Vis. Pattern Recog. Worksh.})
@String(CSVT = {IEEE Trans. Circuit Syst. Video Technol.})

@String(SPL	= {IEEE Sign. Process. Letters})
@String(VR   = {Vis. Res.})
@String(JOV	 = {J. Vis.})
@String(TVC  = {The Vis. Comput.})
@String(JCST  = {J. Comput. Sci. Tech.})
@String(CGF  = {Comput. Graph. Forum})
@String(CVM = {Computational Visual Media})


@String(PAMI  = {IEEE TPAMI})
@String(IJCV  = {IJCV})
@String(CVPR  = {CVPR})
@String(ICCV  = {ICCV})
@String(ECCV  = {ECCV})
@String(NIPS  = {NeurIPS})
@String(ICPR  = {ICPR})
@String(BMVC  =	{BMVC})
@String(TOG   = {ACM TOG})
@String(TIP   = {IEEE TIP})
@String(TVCG  = {IEEE TVCG})
@String(TCSVT = {IEEE TCSVT})
@String(TMM   =	{IEEE TMM})
@String(ACMMM = {ACM MM})
@String(ICME  =	{ICME})
@String(ICASSP=	{ICASSP})
@String(ICIP  = {ICIP})
@String(ACCV  = {ACCV})
@String(ICLR  = {ICLR})
@String(IJCAI = {IJCAI})
@String(PR = {PR})
@String(AAAI = {AAAI})
@String(CVPRW= {CVPRW})
@String(CSVT = {IEEE TCSVT})



@misc{Authors14,
 author = {FirstName LastName},
 title = {The frobnicatable foo filter},
 note = {Face and Gesture submission ID 324. Supplied as supplemental material {\tt fg324.pdf}},
 year = 2014
}

@misc{Authors14b,
 author = {FirstName LastName},
 title = {Frobnication tutorial},
 note = {Supplied as supplemental material {\tt tr.pdf}},
 year = 2014
}

@article{Alpher02,
author = {FirstName Alpher},
title = {Frobnication},
journal = PAMI,
volume = 12,
number = 1,
pages = {234--778},
year = 2002
}

@article{Alpher03,
author = {FirstName Alpher and  FirstName Fotheringham-Smythe},
title = {Frobnication revisited},
journal = {Journal of Foo},
volume = 13,
number = 1,
pages = {234--778},
year = 2003
}

@article{Alpher04,
author = {FirstName Alpher and FirstName Fotheringham-Smythe and FirstName Gamow},
title = {Can a machine frobnicate?},
journal = {Journal of Foo},
volume = 14,
number = 1,
pages = {234--778},
year = 2004
}

@inproceedings{Alpher05,
author = {FirstName Alpher and FirstName Gamow},
title = {Can a computer frobnicate?},
booktitle = CVPR,
pages = {234--778},
year = 2005
}














@article{gupta2020maml,
  title={La-MAML: Look-ahead Meta Learning for Continual Learning},
  author={Gupta, Gunshi and Yadav, Karmesh and Paull, Liam},
  journal={Advances in neural information processing systems},
  year={2020}
}


@article{rusu2018meta,
  title={Meta-learning with latent embedding optimization},
  author={Rusu, Andrei A and Rao, Dushyant and Sygnowski, Jakub and Vinyals, Oriol and Pascanu, Razvan and Osindero, Simon and Hadsell, Raia},
  journal={International Conference on Learning Representations},
  year={2019}
}


@article{vinyals2016matching,
  title={Matching networks for one shot learning},
  author={Vinyals, Oriol and Blundell, Charles and Lillicrap, Timothy and Wierstra, Daan and others},
  journal={Advances in neural information processing systems},
  volume={29},
  year={2016}
}
@article{rajeswaran2019meta,
  title={Meta-learning with implicit gradients},
  author={Rajeswaran, Aravind and Finn, Chelsea and Kakade, Sham M and Levine, Sergey},
  journal={Advances in neural information processing systems},
  volume={32},
  year={2019}
}


% MAML variant

@inproceedings{collins2022maml,
  title={Maml and anil provably learn representations},
  author={Collins, Liam and Mokhtari, Aryan and Oh, Sewoong and Shakkottai, Sanjay},
  booktitle={International Conference on Machine Learning},
  pages={4238--4310},
  year={2022},
  organization={PMLR}
}


@article{lee2021meta,
  title={Meta-learning sparse implicit neural representations},
  author={Lee, Jaeho and Tack, Jihoon and Lee, Namhoon and Shin, Jinwoo},
  journal={Advances in Neural Information Processing Systems},
  volume={34},
  pages={11769--11780},
  year={2021}
}

@inproceedings{obamuyide2019model,
  title={Model-agnostic meta-learning for relation classification with limited supervision},
  author={Obamuyide, Abiola and Vlachos, Andreas},
  year={2019},
  organization={Association for Computational Linguistics}
}

@article{yin2019meta,
  title={Meta-learning without memorization},
  author={Yin, Mingzhang and Tucker, George and Zhou, Mingyuan and Levine, Sergey and Finn, Chelsea},
  journal={International Conference on Learning Representations},
  year={2020}
}


% multi-branch frameworks

@article{liang2022camero,
  title={Camero: Consistency regularized ensemble of perturbed language models with weight sharing},
  author={Liang, Chen and He, Pengcheng and Shen, Yelong and Chen, Weizhu and Zhao, Tuo},
  journal={arXiv preprint arXiv:2204.06625},
  year={2022}
}


@InProceedings{Xie_2022_CVPR,
    author    = {Xie, Pengtao and Du, Xuefeng},
    title     = {Performance-Aw@article{vinyals2016matching,
  title={Matching networks for one shot learning},
  author={Vinyals, Oriol and Blundell, Charles and Lillicrap, Timothy and Wierstra, Daan and others},
  journal={Advances in neural information processing systems},
  volume={29},
  year={2016}
}are Mutual Knowledge Distillation for Improving Neural Architecture Search},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2022},
    pages     = {11922-11932}
}


@inproceedings{kim2021feature,
  title={Feature fusion for online mutual knowledge distillation},
  author={Kim, Jangho and Hyun, Minsung and Chung, Inseop and Kwak, Nojun},
  booktitle={2020 25th International Conference on Pattern Recognition (ICPR)},
  pages={4619--4625},
  year={2021},
  organization={IEEE}
}




% intro

@article{vilalta2002perspective,
  title={A perspective view and survey of meta-learning},
  author={Vilalta, Ricardo and Drissi, Youssef},
  journal={Artificial intelligence review},
  volume={18},
  pages={77--95},
  year={2002},
  publisher={Springer}
}



@inproceedings{szegedy2015going,
  title={Going deeper with convolutions},
  author={Szegedy, Christian and Liu, Wei and Jia, Yangqing and Sermanet, Pierre and Reed, Scott and Anguelov, Dragomir and Erhan, Dumitru and Vanhoucke, Vincent and Rabinovich, Andrew},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={1--9},
  year={2015}
}


@inproceedings{zhang2020auxiliary,
  title={Auxiliary training: Towards accurate and robust models},
  author={Zhang, Linfeng and Yu, Muzhou and Chen, Tong and Shi, Zuoqiang and Bao, Chenglong and Ma, Kaisheng},
  booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
  pages={372--381},
  year={2020}
}



% related work - multi-task learning


@article{yang2016deep,
  title={Deep multi-task representation learning: A tensor factorisation approach},
  author={Yang, Yongxin and Hospedales, Timothy},
  journal={International Conference on Learning Representations (ICLR)},
  year={2017}
}




% regularization %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%


@inproceedings{huang2016deep,
  title={Deep networks with stochastic depth},
  author={Huang, Gao and Sun, Yu and Liu, Zhuang and Sedra, Daniel and Weinberger, Kilian Q},
  booktitle={Computer Vision--ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11--14, 2016, Proceedings, Part IV 14},
  pages={646--661},
  year={2016},
  organization={Springer}
}

@article{yang2020gradaug,
  title={Gradaug: A new regularization method for deep neural networks},
  author={Yang, Taojiannan and Zhu, Sijie and Chen, Chen},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  pages={14207--14218},
  year={2020}
}

% dropout
@article{srivastava2014dropout,
  title={Dropout: a simple way to prevent neural networks from overfitting},
  author={Srivastava, Nitish and Hinton, Geoffrey and Krizhevsky, Alex and Sutskever, Ilya and Salakhutdinov, Ruslan},
  journal={The journal of machine learning research},
  volume={15},
  number={1},
  pages={1929--1958},
  year={2014},
  publisher={JMLR. org}
}



%noise

@article{neelakantan2015adding,
  title={Adding gradient noise improves learning for very deep networks},
  author={Neelakantan, Arvind and Vilnis, Luke and Le, Quoc V and Sutskever, Ilya and Kaiser, Lukasz and Kurach, Karol and Martens, James},
  journal={International Conference on Learning Representations (ICLR)},
  year={2015}
}

@article{hinton2002stochastic,
  title={Stochastic neighbor embedding},
  author={Hinton, Geoffrey E and Roweis, Sam},
  journal={Advances in neural information processing systems},
  volume={15},
  year={2002}
}

% meta noise
@inproceedings{tseng2020regularizing,
  title={Regularizing meta-learning via gradient dropout},
  author={Tseng, Hung-Yu and Chen, Yi-Wen and Tsai, Yi-Hsuan and Liu, Sifei and Lin, Yen-Yu and Yang, Ming-Hsuan},
  booktitle={Proceedings of the Asian Conference on Computer Vision},
  year={2020}
}

@article{lee2020meta,
  title={Meta dropout: Learning to perturb latent features for generalization},
  author={Lee, Hae Beom and Nam, Taewook and Yang, Eunho and Hwang, Sung Ju},
  journal={International Conference on Learning Representations (ICLR)},
  year={2020}
}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% knowledge distillation

@article{hinton2015distilling,
  title={Distilling the knowledge in a neural network},
  author={Hinton, Geoffrey and Vinyals, Oriol and Dean, Jeff},
  journal={Advances in neural information processing systems},
  year={2015}
}
%journal={arXiv preprint arXiv:1503.02531},
@inproceedings{zhang2018deep,
  title={Deep mutual learning},
  author={Zhang, Ying and Xiang, Tao and Hospedales, Timothy M and Lu, Huchuan},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={4320--4328},
  year={2018}
}


% collaborative learning


@article{song2018collaborative,
  title={Collaborative learning for deep neural networks},
  author={Song, Guocong and Chai, Wei},
  journal={Advances in neural information processing systems},
  volume={31},
  year={2018}
}

% one the fly native ensemble

@article{zhu2018knowledge,
  title={Knowledge distillation by on-the-fly native ensemble},
  author={Zhu, Xiatian and Gong, Shaogang and others},
  journal={Advances in neural information processing systems},
  volume={31},
  year={2018}
}






%% gradient-based meta-learning
% meta-learning survey
@article{hospedales2021meta,
  title={Meta-learning in neural networks: A survey},
  author={Hospedales, Timothy and Antoniou, Antreas and Micaelli, Paul and Storkey, Amos},
  journal={IEEE transactions on pattern analysis and machine intelligence},
  volume={44},
  number={9},
  pages={5149--5169},
  year={2021},
  publisher={IEEE}
}


%bilevel optimization
@article{colson2007overview,
  title={An overview of bilevel optimization},
  author={Colson, Beno{\^\i}t and Marcotte, Patrice and Savard, Gilles},
  journal={Annals of operations research},
  volume={153},
  pages={235--256},
  year={2007},
  publisher={Springer}
}



@article{khodak2019adaptive,
  title={Adaptive gradient-based meta-learning methods},
  author={Khodak, Mikhail and Balcan, Maria-Florina F and Talwalkar, Ameet S},
  journal={Advances in Neural Information Processing Systems},
  volume={32},
  year={2019}
}

@inproceedings{finn2017model,
  title={Model-agnostic meta-learning for fast adaptation of deep networks},
  author={Finn, Chelsea and Abbeel, Pieter and Levine, Sergey},
  booktitle={International conference on machine learning},
  pages={1126--1135},
  year={2017},
  organization={PMLR}
}

@article{antoniou2018train,
  title={How to train your MAML},
  author={Antoniou, Antreas and Edwards, Harrison and Storkey, Amos},
  journal={International Conference on Learning Representations (ICLR)},
  year={2018}
}

@article{oh2020boil,
  title={Boil: Towards representation change for few-shot learning},
  author={Oh, Jaehoon and Yoo, Hyungjun and Kim, ChangHwan and Yun, Se-Young},
  journal={International Conference on Learning Representations (ICLR)},
  year={2020}
}


@inproceedings{abbas2022sharp,
  title={Sharp-MAML: Sharpness-Aware Model-Agnostic Meta Learning},
  author={Abbas, Momin and Xiao, Quan and Chen, Lisha and Chen, Pin-Yu and Chen, Tianyi},
  booktitle={International Conference on Machine Learning},
  pages={10--32},
  year={2022},
  organization={PMLR}
}

@article{raghu2019rapid,
  title={Rapid learning or feature reuse? towards understanding the effectiveness of maml},
  author={Raghu, Aniruddh and Raghu, Maithra and Bengio, Samy and Vinyals, Oriol},
  journal={arXiv preprint arXiv:1909.09157},
  year={2019}
}

@article{tan2022transductive,
  title={Transductive Linear Probing: A Novel Framework for Few-Shot Node Classification},
  author={Tan, Zhen and Wang, Song and Ding, Kaize and Li, Jundong and Liu, Huan},
  journal={Learning on Graph Conference (LoG)},
  year={2022}
  }

@inproceedings{zhou2019meta,
  title={Meta-gnn: On few-shot node classification in graph meta-learning},
  author={Zhou, Fan and Cao, Chengtai and Zhang, Kunpeng and Trajcevski, Goce and Zhong, Ting and Geng, Ji},
  booktitle={Proceedings of the 28th ACM International Conference on Information and Knowledge Management},
  pages={2357--2360},
  year={2019}
}

@inproceedings{wang2020graph,
  title={Graph few-shot learning with attribute matching},
  author={Wang, Ning and Luo, Minnan and Ding, Kaize and Zhang, Lingling and Li, Jundong and Zheng, Qinghua},
  booktitle={Proceedings of the 29th ACM International Conference on Information \& Knowledge Management},
  pages={1545--1554},
  year={2020}
}

@article{huang2020graph,
  title={Graph meta learning via local subgraphs},
  author={Huang, Kexin and Zitnik, Marinka},
  journal={Advances in neural information processing systems},
  volume={33},
  pages={5862--5874},
  year={2020}
}


% metric 
@inproceedings{kornblith2019similarity,
  title={Similarity of neural network representations revisited},
  author={Kornblith, Simon and Norouzi, Mohammad and Lee, Honglak and Hinton, Geoffrey},
  booktitle={International conference on machine learning},
  pages={3519--3529},
  year={2019},
  organization={PMLR}
}
% datasets

@article{vinyals2016matching,
  title={Matching networks for one shot learning},
  author={Vinyals, Oriol and Blundell, Charles and Lillicrap, Timothy and Wierstra, Daan and others},
  journal={Advances in neural information processing systems},
  volume={29},
  year={2016}
}

@article{oreshkin2018tadam,
  title={Tadam: Task dependent adaptive metric for improved few-shot learning},
  author={Oreshkin, Boris and Rodr{\'\i}guez L{\'o}pez, Pau and Lacoste, Alexandre},
  journal={Advances in neural information processing systems},
  volume={31},
  year={2018}
}

@article{bertinetto2018meta,
  title={Meta-learning with differentiable closed-form solvers},
  author={Bertinetto, Luca and Henriques, Joao F and Torr, Philip HS and Vedaldi, Andrea},
  journal={arXiv preprint arXiv:1805.08136},
  year={2018}
}

@article{lake2015human,
  title={Human-level concept learning through probabilistic program induction},
  author={Lake, Brenden M and Salakhutdinov, Ruslan and Tenenbaum, Joshua B},
  journal={Science},
  volume={350},
  number={6266},
  pages={1332--1338},
  year={2015},
  publisher={American Association for the Advancement of Science}
}

@article{lee2019learning,
  title={Learning to balance: Bayesian meta-learning for imbalanced and out-of-distribution tasks},
  author={Lee, Hae Beom and Lee, Hayeon and Na, Donghyun and Kim, Saehoon and Park, Minseop and Yang, Eunho and Hwang, Sung Ju},
  journal={arXiv preprint arXiv:1905.12917},
  year={2019}
}
@article{van2008visualizing,
  title={Visualizing data using t-SNE.},
  author={Van der Maaten, Laurens and Hinton, Geoffrey},
  journal={Journal of machine learning research},
  volume={9},
  number={11},
  year={2008}
}

@article{sen2008collective,
  title={Collective classification in network data},
  author={Sen, Prithviraj and Namata, Galileo and Bilgic, Mustafa and Getoor, Lise and Galligher, Brian and Eliassi-Rad, Tina},
  journal={AI magazine},
  volume={29},
  number={3},
  pages={93--93},
  year={2008}
}


}

@article{shchur2018pitfalls,
  title={Pitfalls of graph neural network evaluation},
  author={Shchur, Oleksandr and Mumme, Maximilian and Bojchevski, Aleksandar and G{\"u}nnemann, Stephan},
  journal={arXiv preprint arXiv:1811.05868},
  year={2018}
}

@article{oreshkin2018tadam,
  title={Tadam: Task dependent adaptive metric for improved few-shot learning},
  author={Oreshkin, Boris and Rodr{\'\i}guez L{\'o}pez, Pau and Lacoste, Alexandre},
  journal={Advances in neural information processing systems},
  volume={31},
  year={2018}
}