@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})




@String(PAMI = {IEEE transactions on pattern analysis and machine intelligence})
@String(IJCV = {International journal of computer vision})
@String(CVPR= {Proceedings of the IEEE conference on computer vision and pattern
recognition (CVPR)})
@String(ICCV= {International conference on computer vision (ICCV)})
@String(ECCV= {Proceedings of the European conference on computer vision (ECCV)})
@String(NIPS= {Advances in neural information processing systems})
@String(ICML= {International conference on machine learning (ICML)})
@String(WACV= {Proceedings of the IEEE/CVF winter conference on applications of computer vision (WACV)})
@String(ICPR = {International conference on pattern recognition (ICPR)})
@String(BMVC= {British machine vision conference (BMVC)})
@String(TOG= {ACM transactions on graphics})
@String(TIP  = {IEEE transactions on image processing})
@String(TVCG  = {IEEE transactions on visualization and computer graphics})
@String(TMM  = {IEEE transactions nultimedia})
@String(ACMMM= {ACM international conference multimedia})
@String(ICME = {International conference on multimedia and expo})
@String(ICMM = {International conference on multimedia modeling})
@String(ICASSP=	{IEEE international conference on acoustics, speech and signal processing (ICASSP)})
@String(ICIP = {IEEE international conference on image processing (ICIP)})
@String(ACCV  = {Asian conference on computer vision (ACCV)})
@String(ICLR = {International conference on learning representations (ICLR)})
@String(IJCAI = {International joint conference on artificial intelligence (IJCAI)})
@String(PR   = {Pattern recognition})
@String(AAAI = {AAAI conference on artificial intelligence})
@String(CVPRW= {IEEE conference on computer vision and pattern recognition workshop (CVPRW)})
@String(CSVT = {IEEE Trans. Circuit Syst. Video Technol.})

@String(SPL	= {IEEE signal processing 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(TMI = {IEEE Transactions on medical imaging})
@String(MICCAI = {International conference on medical image computing and computer-assisted intervention (MICCAI)})
@String(ISBI = {IEEE international symposium on biomedical imaging})
@String(ISVC = {Adv. Vis. Comput. – Int. Symp.})
@String(DLMIAMLCLS = {Deep Learn. Med. Image Anal. Multimodal Learn. Clin. Decis. Support})
@String(MIDL = {Medical imaging with deep learning (MIDL)})

@book{Lamport:Book:1989,
 author = {Lamport, Leslie},
 title = {Latex: A Document Preparation System},
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 isbn = {0-201-15790-X},
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@misc{Hinton:arXiv:2015:Distilling,
 title={Distilling the knowledge in a neural network},
 author={Hinton, Geoffrey and Vinyals, Oriol and Dean, Jeff},
 OPTeprinttype={arXiv},
 OPTeprint={1503.02531},
 howpublished={Eprint \href{http://arxiv.org/abs/1503.02531}{arXiv:1503.02531}},
 OPTurl={http://arxiv.org/abs/1503.02531},
 year=2015
}


%
%  $Description: Sample bibliography$ 
% 
%  $Author: ienne $ 
%  $Date: 1995/09/15 15:19:53 $
%  $Revision: 1.3 $
%

@article{blalock2020state,
  title={What is the state of neural network pruning?},
  author={Blalock, Davis and Gonzalez Ortiz, Jose Javier and Frankle, Jonathan and Guttag, John},
  journal={Proceedings of machine learning and systems},
  volume={2},
  pages={129--146},
  year={2020}
}
@inproceedings{molchanov2019importance,
  title={Importance estimation for neural network pruning},
  author={Molchanov, Pavlo and Mallya, Arun and Tyree, Stephen and Frosio, Iuri and Kautz, Jan},
  booktitle=CVPR,
  pages={11264--11272},
  year={2019}
}
@article{yang2022dataset,
  title={Dataset Pruning: Reducing Training Data by Examining Generalization Influence},
  author={Yang, Shuo and Xie, Zeke and Peng, Hanyu and Xu, Min and Sun, Mingming and Li, Ping},
  journal={arXiv preprint arXiv:2205.09329},
  year={2022}
}

@article{toneva2018empirical,
  title={An empirical study of example forgetting during deep neural network learning},
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  year={2018}
}

@article{paul2021deep,
  title={Deep learning on a data diet: Finding important examples early in training},
  author={Paul, Mansheej and Ganguli, Surya and Dziugaite, Gintare Karolina},
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  volume={34},
  pages={20596--20607},
  year={2021}
}
@article{feldman2020turning,
  title={Turning big data into tiny data: Constant-size coresets for k-means, PCA, and projective clustering},
  author={Feldman, Dan and Schmidt, Melanie and Sohler, Christian},
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  year={2020},
  publisher={SIAM}
}
@article{yang2021estimating,
  title={Estimating instance-dependent label-noise transition matrix using dnns},
  author={Yang, Shuo and Yang, Erkun and Han, Bo and Liu, Yang and Xu, Min and Niu, Gang and Liu, Tongliang},
  journal={arXiv preprint arXiv:2105.13001},
  year={2021}
}
@inproceedings{castro2018end,
  title={End-to-end incremental learning},
  author={Castro, Francisco M and Mar{\'\i}n-Jim{\'e}nez, Manuel J and Guil, Nicol{\'a}s and Schmid, Cordelia and Alahari, Karteek},
  booktitle=ECCV,
  pages={233--248},
  year={2018}
}
@article{aljundi2019gradient,
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  journal=NIPS,
  volume={32},
  year={2019}
}
@inproceedings{such2020generative,
  title={Generative teaching networks: Accelerating neural architecture search by learning to generate synthetic training data},
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  booktitle=ICML,
  pages={9206--9216},
  year={2020},
  organization={PMLR}
}
@article{zhao2021dataset,
  title={Dataset Condensation with Gradient Matching.},
  author={Zhao, Bo and Mopuri, Konda Reddy and Bilen, Hakan},
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  number={2},
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  year={2021}
}

@inproceedings{sucholutsky2021soft,
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  author={Sucholutsky, Ilia and Schonlau, Matthias},
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  year={2021},
  organization={IEEE}
}
@article{wang2018dataset,
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}
@inproceedings{jha2020kvasir,
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  organization={Springer}
}
@article{bernal2015wm,
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  publisher={Elsevier}
}
@article{lecun1998gradient,
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@article{krizhevsky2009learning,
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@article{le2015tiny,
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}
@article{loshchilov2017decoupled,
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@inproceedings{fan2020pranet,
  title={Pranet: Parallel reverse attention network for polyp segmentation},
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  booktitle=MICCAI,
  pages={263--273},
  year={2020},
  organization={Springer}
}
@article{wang2022pvt,
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  journal=CVM,
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  year={2022},
  publisher={Springer}
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@inproceedings{rahman2023medical,
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  author={Rahman, Md Mostafijur and Marculescu, Radu},
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@inproceedings{sandler2018mobilenetv2,
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  year={2018}
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@inproceedings{he2016deep,
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@article{blondel2008fast,
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@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
}


%
%  $Description: Sample bibliography$ 
% 
%  $Author: ienne $ 
%  $Date: 1995/09/15 15:19:53 $
%  $Revision: 1.3 $
%

@article{blalock2020state,
  title={What is the state of neural network pruning?},
  author={Blalock, Davis and Gonzalez Ortiz, Jose Javier and Frankle, Jonathan and Guttag, John},
  journal={Mach. Learn. Syst.},
  volume={2},
  pages={129--146},
  year={2020}
}
@inproceedings{molchanov2019importance,
  title={Importance estimation for neural network pruning},
  author={Molchanov, Pavlo and Mallya, Arun and Tyree, Stephen and Frosio, Iuri and Kautz, Jan},
  booktitle=CVPR,
  pages={11264--11272},
  year={2019}
}


@article{paul2021deep,
  title={Deep learning on a data diet: Finding important examples early in training},
  author={Paul, Mansheej and Ganguli, Surya and Dziugaite, Gintare Karolina},
  journal=NIPS,
  volume={34},
  pages={20596--20607},
  year={2021}
}
@article{feldman2020turning,
  title={Turning big data into tiny data: Constant-size coresets for k-means, PCA, and projective clustering},
  author={Feldman, Dan and Schmidt, Melanie and Sohler, Christian},
  journal={SIAM J. Comput.},
  volume={49},
  number={3},
  pages={601--657},
  year={2020},
  publisher={SIAM}
}
@article{yang2021estimating,
  title={Estimating instance-dependent label-noise transition matrix using dnns},
  author={Yang, Shuo and Yang, Erkun and Han, Bo and Liu, Yang and Xu, Min and Niu, Gang and Liu, Tongliang},
  journal={arXiv preprint arXiv:2105.13001},
  year={2021}
}

@article{moghbel2018review,
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}
@inproceedings{ronneberger2015u,
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}

@inproceedings{huang2020unet,
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}
@inproceedings{lou2021dc,
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}
@article{chen2021transunet,
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}

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@article{cao2021swin,
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}

@article{chen2017deeplab,
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