Yaoyao Liu (PhD Student)

Personal Information

Publications

2024

Conference paper

Y. Liu, Y. Li, B. Schiele, and Q. Sun

“Wakening Past Concepts without Past Data: Class-Incremental Learning from Online Placebos,” in WACV 2024, IEEE Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA, 2024.

mehr

BibTeX

@inproceedings{Liu_WACV24,
TITLE = {Wakening Past Concepts without Past Data: {C}lass-Incremental Learning from Online Placebos},
AUTHOR = {Liu, Yaoyao and Li, Yingying and Schiele, Bernt and Sun, Qianru},
LANGUAGE = {eng},
ISBN = {979-8-3503-1892-0},
DOI = {10.1109/WACV57701.2024.00222},
PUBLISHER = {IEEE},
YEAR = {2024},
MARGINALMARK = {$\bullet$},
DATE = {2024},
BOOKTITLE = {WACV 2024, IEEE Winter Conference on Applications of Computer Vision},
PAGES = {2215--2224},
ADDRESS = {Waikoloa, HI, USA},
}

Endnote

%0 Conference Proceedings
%A Liu, Yaoyao
%A Li, Yingying
%A Schiele, Bernt
%A Sun, Qianru
%+ Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
External Organizations
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
External Organizations
%T Wakening Past Concepts without Past Data: Class-Incremental Learning from Online Placebos : 
%G eng
%U http://hdl.handle.net/21.11116/0000-0010-445F-8
%R 10.1109/WACV57701.2024.00222
%D 2024
%B IEEE Winter Conference on Applications of Computer Vision
%Z date of event: 2024-01-03 - 2024-01-08
%C Waikoloa, HI, USA
%B WACV 2024
%P 2215 - 2224
%I IEEE
%@ 979-8-3503-1892-0

2023

Conference paper

Z. Luo, Y. Liu, B. Schiele, and Q. Sun

“Class-Incremental Exemplar Compression for Class-Incremental Learning,” in 36th IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2023), Vancouver, Canada, 2023.

mehr

BibTeX

@inproceedings{LuoCVPR23,
TITLE = {Class-Incremental Exemplar Compression for Class-Incremental Learning},
AUTHOR = {Luo, Zilin and Liu, Yaoyao and Schiele, Bernt and Sun, Qianru},
LANGUAGE = {eng},
ISBN = {979-8-3503-0129-8},
DOI = {10.1109/CVPR52729.2023.01094},
PUBLISHER = {IEEE},
YEAR = {2023},
MARGINALMARK = {$\bullet$},
DATE = {2023},
BOOKTITLE = {36th IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2023)},
PAGES = {11371--11380},
ADDRESS = {Vancouver, Canada},
}

Endnote

%0 Conference Proceedings
%A Luo, Zilin
%A Liu, Yaoyao
%A Schiele, Bernt
%A Sun, Qianru
%+ External Organizations
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
External Organizations
%T Class-Incremental Exemplar Compression for Class-Incremental Learning : 
%G eng
%U http://hdl.handle.net/21.11116/0000-000D-0C76-2
%R 10.1109/CVPR52729.2023.01094
%D 2023
%B 36th IEEE/CVF Conference on Computer Vision and Pattern Recognition
%Z date of event: 2023-06-18 - 2023-06-23
%C Vancouver, Canada
%B 36th IEEE/CVF Conference on Computer Vision and Pattern Recognition
%P 11371 - 11380
%I IEEE
%@ 979-8-3503-0129-8

Conference paper

Y. Liu, B. Schiele, A. Vedaldi, and C. Rupprecht

“Continual Detection Transformer for Incremental Object Detection,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2023), Vancouver, Canada, 2023.

mehr

BibTeX

@inproceedings{LiuCVPR23,
TITLE = {Continual Detection Transformer for Incremental Object Detection},
AUTHOR = {Liu, Yaoyao and Schiele, Bernt and Vedaldi, Andrea and Rupprecht, Christian},
LANGUAGE = {eng},
ISBN = {979-8-3503-0129-8},
DOI = {10.1109/CVPR52729.2023.02279},
PUBLISHER = {IEEE},
YEAR = {2023},
MARGINALMARK = {$\bullet$},
DATE = {2023},
BOOKTITLE = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2023)},
PAGES = {23799--23808},
ADDRESS = {Vancouver, Canada},
}

Endnote

%0 Conference Proceedings
%A Liu, Yaoyao
%A Schiele, Bernt
%A Vedaldi, Andrea
%A Rupprecht, Christian
%+ Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
External Organizations
External Organizations
%T Continual Detection Transformer for Incremental Object Detection : 
%G eng
%U http://hdl.handle.net/21.11116/0000-000D-0C70-8
%R 10.1109/CVPR52729.2023.02279
%D 2023
%B 36th IEEE/CVF Conference on Computer Vision and Pattern Recognition
%Z date of event: 2023-06-18 - 2023-06-23
%C Vancouver, Canada
%B IEEE/CVF Conference on Computer Vision and Pattern Recognition
%P 23799 - 23808
%I IEEE
%@ 979-8-3503-0129-8

Conference paper

Y. Liu, Y. Li, B. Schiele, and Q. Sun

“Online Hyperparameter Optimization for Class-Incremental Learning,” in Proceedings of the 37th AAAI Conference on Artificial Intelligence, Washington, DC, USA, 2023.

mehr

BibTeX

@inproceedings{Liu_AAAI23,
TITLE = {Online Hyperparameter Optimization for Class-Incremental Learning},
AUTHOR = {Liu, Yaoyao and Li, Yingying and Schiele, Bernt and Sun, Qianru},
LANGUAGE = {eng},
ISBN = {978-1-57735-880-0},
DOI = {10.1609/aaai.v37i7.26070},
PUBLISHER = {AAAI},
YEAR = {2023},
MARGINALMARK = {$\bullet$},
DATE = {2023},
BOOKTITLE = {Proceedings of the 37th AAAI Conference on Artificial Intelligence},
PAGES = {8906--8913},
ADDRESS = {Washington, DC, USA},
}

Endnote

%0 Conference Proceedings
%A Liu, Yaoyao
%A Li, Yingying
%A Schiele, Bernt
%A Sun, Qianru
%+ Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
External Organizations
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
External Organizations
%T Online Hyperparameter Optimization for Class-Incremental Learning : 
%G eng
%U http://hdl.handle.net/21.11116/0000-000B-C9EC-9
%R 10.1609/aaai.v37i7.26070 
%D 2023
%B 37th AAAI Conference on Artificial Intelligence
%Z date of event: 2023-02-07 - 2023-02-14
%C Washington, DC, USA
%B Proceedings of the 37th AAAI Conference on Artificial Intelligence
%P 8906 - 8913
%I AAAI
%@ 978-1-57735-880-0

Thesis

D2IMPR-CS

Y. Liu

“Learning from Imperfect Data Incremental Learning and Few-shot Learning,” Universität des Saarlandes, Saarbrücken, 2023.

mehr

BibTeX

@phdthesis{Liu_PhD2023,
TITLE = {Learning from Imperfect Data Incremental Learning and Few-shot Learning},
AUTHOR = {Liu, Yaoyao},
LANGUAGE = {eng},
URL = {urn:nbn:de:bsz:291--ds-395111},
DOI = {10.22028/D291-39511},
SCHOOL = {Universit{\"a}t des Saarlandes},
ADDRESS = {Saarbr{\"u}cken},
YEAR = {2023},
MARGINALMARK = {$\bullet$},
DATE = {2023},
}

Endnote

%0 Thesis
%A Liu, Yaoyao
%Y Schiele, Bernt
%A referee: Tuytelaars, Tinne
%A referee: Tang, Siyu
%+ Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
International Max Planck Research School, MPI for Informatics, Max Planck Society
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
External Organizations
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
%T Learning from Imperfect Data Incremental Learning and Few-shot Learning : 
%G eng
%U http://hdl.handle.net/21.11116/0000-000D-0392-A
%U urn:nbn:de:bsz:291--ds-395111
%R 10.22028/D291-39511
%F OTHER: hdl:20.500.11880/35715
%I Universit&#228;t des Saarlandes
%C Saarbr&#252;cken
%D 2023
%P xiv, 172 p
%V phd
%9 phd
%U http://dx.doi.org/10.22028/D291-39511

2022

Article

Q. Sun, Y. Liu, Z. Chen, T.-S. Chua, and B. Schiele

“Meta-Transfer Learning through Hard Tasks,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 3, 2022.

mehr

BibTeX

@article{Sun__TPAMI2020,
TITLE = {Meta-Transfer Learning through Hard Tasks},
AUTHOR = {Sun, Qianru and Liu, Yaoyao and Chen, Zhaozheng and Chua, Tat-Seng and Schiele, Bernt},
LANGUAGE = {eng},
ISSN = {0162-8828},
DOI = {10.1109/TPAMI.2020.3018506},
PUBLISHER = {IEEE},
ADDRESS = {Piscataway, NJ},
YEAR = {2022},
DATE = {2022},
JOURNAL = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
VOLUME = {44},
NUMBER = {3},
PAGES = {1443--1456},
}

Endnote

%0 Journal Article
%A Sun, Qianru
%A Liu, Yaoyao
%A Chen, Zhaozheng
%A Chua, Tat-Seng
%A Schiele, Bernt
%+ External Organizations
External Organizations
External Organizations
External Organizations
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
%T Meta-Transfer Learning through Hard Tasks : 
%G eng
%U http://hdl.handle.net/21.11116/0000-0005-5552-F
%R 10.1109/TPAMI.2020.3018506
%7 2020
%D 2022
%J IEEE Transactions on Pattern Analysis and Machine Intelligence
%O IEEE Trans. Pattern Anal. Mach. Intell.
%V 44
%N 3
%& 1443
%P 1443 - 1456
%I IEEE
%C Piscataway, NJ
%@ false

2021

Conference paper

Y. Liu, B. Schiele, and Q. Sun

“RMM: Reinforced Memory Management for Class-Incremental Learning,” in Advances in Neural Information Processing Systems 34 (NeurIPS 2021), Virtual, 2021.

mehr

BibTeX

@inproceedings{Liu_Neurips2021,
TITLE = {{RMM}: {R}einforced Memory Management for Class-Incremental Learning},
AUTHOR = {Liu, Yaoyao and Schiele, Bernt and Sun, Qianru},
LANGUAGE = {eng},
PUBLISHER = {Curran Associates, Inc.},
YEAR = {2021},
BOOKTITLE = {Advances in Neural Information Processing Systems 34 (NeurIPS 2021)},
EDITOR = {Ranzato, M. and Beygelzimer, A. and Liang, P. S. and Vaughan, J. W. and Dauphin, Y.},
PAGES = {3478--3490},
ADDRESS = {Virtual},
}

Endnote

%0 Conference Proceedings
%A Liu, Yaoyao
%A Schiele, Bernt
%A Sun, Qianru
%+ Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
External Organizations
%T RMM: Reinforced Memory Management for Class-Incremental Learning : 
%G eng
%U http://hdl.handle.net/21.11116/0000-0009-A78D-C
%D 2021
%B 35th Conference on Neural Information Processing Systems
%Z date of event: 2021-12-07 - 2021-12-07
%C Virtual
%B Advances in Neural Information Processing Systems 34
%E Ranzato, M.; Beygelzimer, A.; Liang, P. S.; Vaughan, J. W.; Dauphin, Y.
%P 3478 - 3490
%I Curran Associates, Inc.
%U https://proceedings.neurips.cc/paper/2021/file/1cbcaa5abbb6b70f378a3a03d0c26386-Paper.pdf

Article

X. Li, J. Huang, Y. Liu, Q. Zhou, S. Zheng, B. Schiele, and Q. Sun

“Learning to Teach and Learn for Semi-supervised Few-shot Image Classification,” Computer Vision and Image Understanding, vol. 212, 2021.

mehr

BibTeX

@article{Li2021,
TITLE = {Learning to Teach and Learn for Semi-supervised Few-shot Image Classification},
AUTHOR = {Li, Xinzhe and Huang, Jianqiang and Liu, Yaoyao and Zhou, Qin and Zheng, Shibao and Schiele, Bernt and Sun, Qianru},
LANGUAGE = {eng},
ISSN = {1077-3142},
DOI = {10.1016/j.cviu.2021.103270},
PUBLISHER = {Elsevier},
ADDRESS = {Amsterdam},
YEAR = {2021},
DATE = {2021},
JOURNAL = {Computer Vision and Image Understanding},
VOLUME = {212},
EID = {103270},
}

Endnote

%0 Journal Article
%A Li, Xinzhe
%A Huang, Jianqiang
%A Liu, Yaoyao
%A Zhou, Qin
%A Zheng, Shibao
%A Schiele, Bernt
%A Sun, Qianru
%+ External Organizations
External Organizations
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
External Organizations
External Organizations
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
External Organizations
%T Learning to Teach and Learn for Semi-supervised Few-shot Image Classification : 
%G eng
%U http://hdl.handle.net/21.11116/0000-0009-7BA7-1
%R 10.1016/j.cviu.2021.103270
%7 2021
%D 2021
%J Computer Vision and Image Understanding
%V 212
%Z sequence number: 103270
%I Elsevier
%C Amsterdam
%@ false

Conference paper

Y. Liu, B. Schiele, and Q. Sun

“Adaptive Aggregation Networks for Class-Incremental Learning,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021), Nashville, TN, USA (Virtual), 2021.

mehr

BibTeX

@inproceedings{Liu_arXiv2010.05063,
TITLE = {Adaptive Aggregation Networks for Class-Incremental Learning},
AUTHOR = {Liu, Yaoyao and Schiele, Bernt and Sun, Qianru},
LANGUAGE = {eng},
ISBN = {978-1-6654-4509-2},
DOI = {10.1109/CVPR46437.2021.00257},
PUBLISHER = {IEEE},
YEAR = {2021},
BOOKTITLE = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021)},
PAGES = {2544--2553},
ADDRESS = {Nashville, TN, USA (Virtual)},
}

Endnote

%0 Conference Proceedings
%A Liu, Yaoyao
%A Schiele, Bernt
%A Sun, Qianru
%+ Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
External Organizations
%T Adaptive Aggregation Networks for Class-Incremental Learning : 
%G eng
%U http://hdl.handle.net/21.11116/0000-0007-80F3-5
%R 10.1109/CVPR46437.2021.00257
%D 2021
%B 34th IEEE Conference on Computer Vision and Pattern Recognition
%Z date of event: 2021-06-19 - 2021-06-25
%C Nashville, TN, USA  (Virtual)
%B IEEE/CVF Conference on Computer Vision and Pattern Recognition
%P 2544 - 2553
%I IEEE
%@ 978-1-6654-4509-2

Article

Y. Liu, Q. Sun, X. He, A.-A. Liu, Y. Su, and T.-S. Chua

“Generating Face Images With Attributes for Free,” IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 6, 2021.

mehr

BibTeX

@article{Liu2021b,
TITLE = {Generating Face Images With Attributes for Free},
AUTHOR = {Liu, Yaoyao and Sun, Qianru and He, Xiangnan and Liu, An-An and Su, Yuting and Chua, Tat-Seng},
LANGUAGE = {eng},
ISSN = {2162-237X},
DOI = {10.1109/TNNLS.2020.3007790},
PUBLISHER = {IEEE},
ADDRESS = {Piscataway, NJ},
YEAR = {2021},
DATE = {2021},
JOURNAL = {IEEE Transactions on Neural Networks and Learning Systems},
VOLUME = {32},
NUMBER = {6},
PAGES = {2733--2743},
}

Endnote

%0 Journal Article
%A Liu, Yaoyao
%A Sun, Qianru
%A He, Xiangnan
%A Liu, An-An
%A Su, Yuting
%A Chua, Tat-Seng
%+ Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
External Organizations
External Organizations
External Organizations
External Organizations
External Organizations
%T Generating Face Images With Attributes for Free : 
%G eng
%U http://hdl.handle.net/21.11116/0000-0008-D9B6-6
%R 10.1109/TNNLS.2020.3007790
%7 2021
%D 2021
%J IEEE Transactions on Neural Networks and Learning Systems
%V 32
%N 6
%& 2733
%P 2733 - 2743
%I IEEE
%C Piscataway, NJ
%@ false

2020

Conference paper

Y. Liu, B. Schiele, and Q. Sun

“An Ensemble of Epoch-wise Empirical Bayes for Few-shot Learning,” in Computer Vision -- ECCV 2020, Glasgow, UK, 2020.

mehr

BibTeX

@inproceedings{Liu_ECCV2020,
TITLE = {An Ensemble of Epoch-wise Empirical Bayes for Few-shot Learning},
AUTHOR = {Liu, Yaoyao and Schiele, Bernt and Sun, Qianru},
LANGUAGE = {eng},
ISBN = {978-3-030-58516-7},
DOI = {10.1007/978-3-030-58517-4_24},
PUBLISHER = {Springer},
YEAR = {2020},
DATE = {2020},
BOOKTITLE = {Computer Vision -- ECCV 2020},
EDITOR = {Vedaldi, Andrea and Bischof, Horst and Brox, Thomas and Frahm, Jan-Michael},
PAGES = {404--421},
SERIES = {Lecture Notes in Computer Science},
VOLUME = {12361},
ADDRESS = {Glasgow, UK},
}

Endnote

%0 Conference Proceedings
%A Liu, Yaoyao
%A Schiele, Bernt
%A Sun, Qianru
%+ Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
External Organizations
%T An Ensemble of Epoch-wise Empirical Bayes for Few-shot Learning : 
%G eng
%U http://hdl.handle.net/21.11116/0000-0006-EFBE-8
%R 10.1007/978-3-030-58517-4_24
%D 2020
%B 16th European Conference on Computer Vision
%Z date of event: 2020-08-23 - 2020-08-28
%C Glasgow, UK
%B Computer Vision -- ECCV 2020
%E Vedaldi, Andrea; Bischof, Horst; Brox, Thomas; Frahm, Jan-Michael
%P 404 - 421
%I Springer
%@ 978-3-030-58516-7
%B Lecture Notes in Computer Science
%N 12361

Conference paper

Y. Liu, Y. Su, A.-A. Liu, B. Schiele, and Q. Sun

“Mnemonics Training: Multi-Class Incremental Learning Without Forgetting,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2020), Seattle, WA, USA (Virtual), 2020.

mehr

BibTeX

@inproceedings{Liu_2020_CVPR,
TITLE = {Mnemonics Training: {M}ulti-Class Incremental Learning Without Forgetting},
AUTHOR = {Liu, Yaoyao and Su, Yuting and Liu, An-An and Schiele, Bernt and Sun, Qianru},
LANGUAGE = {eng},
ISBN = {978-1-7281-7168-5},
DOI = {10.1109/CVPR42600.2020.01226},
PUBLISHER = {IEEE},
YEAR = {2020},
BOOKTITLE = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2020)},
PAGES = {12242--12251},
ADDRESS = {Seattle, WA, USA (Virtual)},
}

Endnote

%0 Conference Proceedings
%A Liu, Yaoyao
%A Su, Yuting
%A Liu, An-An
%A Schiele, Bernt
%A Sun, Qianru
%+ Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
External Organizations
External Organizations
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
External Organizations
%T Mnemonics Training: Multi-Class Incremental Learning Without Forgetting : 
%G eng
%U http://hdl.handle.net/21.11116/0000-0006-EFD1-1
%R 10.1109/CVPR42600.2020.01226
%D 2020
%B 33rd IEEE Conference on Computer Vision and Pattern Recognition
%Z date of event: 2020-06-14 - 2020-06-19
%C Seattle, WA, USA (Virtual)
%B IEEE/CVF Conference on Computer Vision and Pattern Recognition
%P 12242 - 12251
%I IEEE
%@ 978-1-7281-7168-5

2019

Conference paper

X. Li, Q. Sun, Y. Liu, Q. Zhou, S. Zheng, T.-S. Chua, and B. Schiele

“Learning to Self-Train for Semi-Supervised Few-Shot Classification,” in Advances in Neural Information Processing Systems 32 (NeurIPS 2019), Vancouver, Canada, 2019.

mehr

BibTeX

@inproceedings{Li_NeurIPs2019,
TITLE = {Learning to Self-Train for Semi-Supervised Few-Shot Classification},
AUTHOR = {Li, Xinzhe and Sun, Qianru and Liu, Yaoyao and Zhou, Qin and Zheng, Shibao and Chua, Tat-Seng and Schiele, Bernt},
LANGUAGE = {eng},
URL = {http://papers.nips.cc/paper/9216-learning-to-self-train-for-semi-supervised-few-shot-classification.pdf},
PUBLISHER = {Curran Associates, Inc.},
YEAR = {2019},
BOOKTITLE = {Advances in Neural Information Processing Systems 32 (NeurIPS 2019)},
EDITOR = {Wallach, H. and Larochelle, H. and Beygelzimer, A. and d'Alch{\'e}-Buc, F. and Fox, E. and Garnett, R.},
PAGES = {10276--10286},
ADDRESS = {Vancouver, Canada},
}

Endnote

%0 Conference Proceedings
%A Li, Xinzhe
%A Sun, Qianru
%A Liu, Yaoyao
%A Zhou, Qin
%A Zheng, Shibao
%A Chua, Tat-Seng
%A Schiele, Bernt
%+ External Organizations
External Organizations
External Organizations
External Organizations
External Organizations
External Organizations
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
%T Learning to Self-Train for Semi-Supervised Few-Shot Classification : 
%G eng
%U http://hdl.handle.net/21.11116/0000-0005-551B-E
%U http://papers.nips.cc/paper/9216-learning-to-self-train-for-semi-supervised-few-shot-classification.pdf
%D 2019
%B 33rd Conference on Neural Information Processing Systems
%Z date of event: 2019-12-08 - 2019-12-14
%C Vancouver, Canada
%B Advances in Neural Information Processing Systems 32 
%E Wallach, H.; Larochelle, H.; Beygelzimer, A.; d'Alch&#233;-Buc, F.; Fox, E.; Garnett, R.
%P 10276 - 10286
%I Curran Associates, Inc.

Conference paper

Q. Sun, Y. Liu, T.-S. Chua, and B. Schiele

“Meta-Transfer Learning for Few-Shot Learning,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2019), Long Beach, CA, USA, 2019.

mehr

BibTeX

@inproceedings{Sun_CVPR2019,
TITLE = {Meta-Transfer Learning for Few-Shot Learning},
AUTHOR = {Sun, Qianru and Liu, Yaoyao and Chua, Tat-Seng and Schiele, Bernt},
LANGUAGE = {eng},
ISBN = {978-1-7281-3293-8},
DOI = {10.1109/CVPR.2019.00049},
PUBLISHER = {IEEE},
YEAR = {2019},
BOOKTITLE = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2019)},
PAGES = {403--412},
ADDRESS = {Long Beach, CA, USA},
}

Endnote

%0 Conference Proceedings
%A Sun, Qianru
%A Liu, Yaoyao
%A Chua, Tat-Seng
%A Schiele, Bernt
%+ Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
External Organizations
External Organizations
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
%T Meta-Transfer Learning for Few-Shot Learning : 
%G eng
%U http://hdl.handle.net/21.11116/0000-0003-9604-F
%R 10.1109/CVPR.2019.00049
%D 2019
%B 32nd IEEE Conference on Computer Vision and Pattern Recognition
%Z date of event: 2019-06-16 - 2019-06-20
%C Long Beach, CA, USA
%B IEEE/CVF Conference on Computer Vision and Pattern Recognition 
%P 403 - 412
%I IEEE
%@ 978-1-7281-3293-8

Paper

Y. Liu, Q. Sun, A.-A. Liu, Y. Su, B. Schiele, and T.-S. Chua

“LCC: Learning to Customize and Combine Neural Networks for Few-Shot Learning,” 2019. [Online]. Available: http://arxiv.org/abs/1904.08479.

mehr

Abstract

Meta-learning has been shown to be an effective strategy for few-shot
learning. The key idea is to leverage a large number of similar few-shot tasks
in order to meta-learn how to best initiate a (single) base-learner for novel
few-shot tasks. While meta-learning how to initialize a base-learner has shown
promising results, it is well known that hyperparameter settings such as the
learning rate and the weighting of the regularization term are important to
achieve best performance. We thus propose to also meta-learn these
hyperparameters and in fact learn a time- and layer-varying scheme for learning
a base-learner on novel tasks. Additionally, we propose to learn not only a
single base-learner but an ensemble of several base-learners to obtain more
robust results. While ensembles of learners have shown to improve performance
in various settings, this is challenging for few-shot learning tasks due to the
limited number of training samples. Therefore, our approach also aims to
meta-learn how to effectively combine several base-learners. We conduct
extensive experiments and report top performance for five-class few-shot
recognition tasks on two challenging benchmarks: miniImageNet and
Fewshot-CIFAR100 (FC100).

BibTeX

@online{Liu_arXiv1904.08479,
TITLE = {{LCC}: {L}earning to Customize and Combine Neural Networks for Few-Shot Learning},
AUTHOR = {Liu, Yaoyao and Sun, Qianru and Liu, An-An and Su, Yuting and Schiele, Bernt and Chua, Tat-Seng},
LANGUAGE = {eng},
URL = {http://arxiv.org/abs/1904.08479},
EPRINT = {1904.08479},
EPRINTTYPE = {arXiv},
YEAR = {2019},
ABSTRACT = {Meta-learning has been shown to be an effective strategy for few-shot<br>learning. The key idea is to leverage a large number of similar few-shot tasks<br>in order to meta-learn how to best initiate a (single) base-learner for novel<br>few-shot tasks. While meta-learning how to initialize a base-learner has shown<br>promising results, it is well known that hyperparameter settings such as the<br>learning rate and the weighting of the regularization term are important to<br>achieve best performance. We thus propose to also meta-learn these<br>hyperparameters and in fact learn a time- and layer-varying scheme for learning<br>a base-learner on novel tasks. Additionally, we propose to learn not only a<br>single base-learner but an ensemble of several base-learners to obtain more<br>robust results. While ensembles of learners have shown to improve performance<br>in various settings, this is challenging for few-shot learning tasks due to the<br>limited number of training samples. Therefore, our approach also aims to<br>meta-learn how to effectively combine several base-learners. We conduct<br>extensive experiments and report top performance for five-class few-shot<br>recognition tasks on two challenging benchmarks: miniImageNet and<br>Fewshot-CIFAR100 (FC100).<br>},
}

Endnote

%0 Report
%A Liu, Yaoyao
%A Sun, Qianru
%A Liu, An-An
%A Su, Yuting
%A Schiele, Bernt
%A Chua, Tat-Seng
%+ External Organizations
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
External Organizations
External Organizations
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
External Organizations
%T LCC: Learning to Customize and Combine Neural Networks for Few-Shot
  Learning : 
%G eng
%U http://hdl.handle.net/21.11116/0000-0003-BAF9-3
%U http://arxiv.org/abs/1904.08479
%D 2019
%X   Meta-learning has been shown to be an effective strategy for few-shot<br>learning. The key idea is to leverage a large number of similar few-shot tasks<br>in order to meta-learn how to best initiate a (single) base-learner for novel<br>few-shot tasks. While meta-learning how to initialize a base-learner has shown<br>promising results, it is well known that hyperparameter settings such as the<br>learning rate and the weighting of the regularization term are important to<br>achieve best performance. We thus propose to also meta-learn these<br>hyperparameters and in fact learn a time- and layer-varying scheme for learning<br>a base-learner on novel tasks. Additionally, we propose to learn not only a<br>single base-learner but an ensemble of several base-learners to obtain more<br>robust results. While ensembles of learners have shown to improve performance<br>in various settings, this is challenging for few-shot learning tasks due to the<br>limited number of training samples. Therefore, our approach also aims to<br>meta-learn how to effectively combine several base-learners. We conduct<br>extensive experiments and report top performance for five-class few-shot<br>recognition tasks on two challenging benchmarks: miniImageNet and<br>Fewshot-CIFAR100 (FC100).<br>
%K Computer Science, Computer Vision and Pattern Recognition, cs.CV,Computer Science, Learning, cs.LG,Statistics, Machine Learning, stat.ML