Built on

C. E. Shannon, “A mathematical theory of communication,” The Bell System Technical Journal , vol. 27, no. 3, pp. 379–423, 7 1948
1948
Earlier work this paper cites.
L. K. Hansen and P. Salamon, “Neural network ensembles,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 12, no. 10, pp. 993–1001, Oct. 1990
1990
Earlier work this paper cites.
D. Cohn, L. Atlas, and R. Ladner, “Improving generalization with active learning,” Mach. Learn. , vol. 15, no. 2, pp. 201–221, May 1994
1994
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T. G. Dietterich, “Ensemble methods in machine learning,” in Multiple Classifier Systems, LBCS-1857 . Springer, 2000, pp. 1–15
2000
Earlier work this paper cites.
G. Schohn and D. Cohn, “Less is more: Active learning with support vector machines,” in ICML , 2000, pp. 839–846
2000
Earlier work this paper cites.
A. Krizhevsky, “Learning multiple layers of features from tiny images,” Tech. Rep., 2009
2009
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in CVPR , 2009
2009
Earlier work this paper cites.
B. Settles, “Active learning literature survey,” Tech. Rep., 2010
2010
Earlier work this paper cites.
M. Everingham, L. Gool, C. K. Williams, J. Winn, and A. Zisserman, “The pascal visual object classes (voc) challenge,” IJCV , vol. 88, no. 2, p. 303–338, Jun. 2010
2010
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N. Houlsby, F. Huszár, Z. Ghahramani, and M. Lengyel, “Bayesian Active Learning for Classification and Preference Learning,” arXiv e-prints , p. arXiv:1112.5745, Dec 2011
Original
2011
Earlier work this paper cites.
A. Lapedriza, H. Pirsiavash, Z. Bylinskii, and A. Torralba, “Are all training examples equally valuable?” arXiv e-prints , Nov 2013
2013
Earlier work this paper cites.
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, “Microsoft coco: Common objects in context,” in ECCV , 2014
2014
Earlier work this paper cites.
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A simple way to prevent neural networks from overfitting,” in ICML , 2014
2014
Earlier work this paper cites.
Y. Gal and Z. Ghahramani, “Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning,” arXiv e-prints , p. arXiv:1506.02142, Jun 2015
Original
2015
Earlier work this paper cites.
Y. Gal and Z. Ghahramani, “Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference,” arXiv e-prints , Jun 2015
2015
Earlier work this paper cites.
E. Gavves, T. Mensink, T. Tommasi, C. G. M. Snoek, and T. Tuytelaars, “Active transfer learning with zero-shot priors: Reusing past datasets for future tasks,” in ICCV , Dec 2015, pp. 2731–2739
2015
Earlier work this paper cites.
K. Konyushkova, R. Sznitman, and P. Fua, “Introducing geometry in active learning for image segmentation,” in ICCV , 2015
2015
Earlier work this paper cites.
Y. Gal, “Uncertainty in deep learning,” Ph.D. dissertation, University of Cambridge, 2016
2016
Earlier work this paper cites.
W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg, “Ssd: Single shot multibox detector,” in ECCV , 2016
2016
Earlier work this paper cites.
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in CVPR , 2016
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in CVPR , 2016
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Identity Mappings in Deep Residual Networks,” arXiv e-prints , p. arXiv:1603.05027, Mar 2016
Original
2016
Earlier work this paper cites.

Similar

G. Huang, Z. Liu, L. van der Maaten, and K. Q. Weinberger, “Densely Connected Convolutional Networks,” arXiv e-prints , p. arXiv:1608.06993, Aug 2016
Original
2016
Cited alongside, same era.
D. Golovin, B. Solnik, S. Moitra, G. Kochanski, J. Karro, and D. Sculley, “Google vizier: A service for black-box optimization,” in SIGKDD , 2017
2017
Cited alongside, same era.
C. Sun, A. Shrivastava, S. Singh, and A. Gupta, “Revisiting Unreasonable Effectiveness of Data in Deep Learning Era,” arXiv e-prints , p. arXiv:1707.02968, Jul 2017
Original
2017
Cited alongside, same era.
B. Lakshminarayanan, A. Pritzel, and C. Blundell, “Simple and scalable predictive uncertainty estimation using deep ensembles,” in NIPS , 2017
2017
Cited alongside, same era.
Y. Gal, R. Islam, and Z. Ghahramani, “Deep Bayesian Active Learning with Image Data,” arXiv e-prints , p. arXiv:1703.02910, Mar 2017

Then

S. Roy, A. Unmesh, and V. P. Namboodiri, “Deep active learning for object detection,” in BMVC , 2018
2018
Later among the works it cites.
K. Vodrahalli, K. Li, and J. Malik, “Are All Training Examples Created Equal? An Empirical Study,” arXiv e-prints , p. arXiv:1811.12569, Nov 2018
Original
2018
Later among the works it cites.
M. Toneva, A. Sordoni, R. Tachet des Combes, A. Trischler, Y. Bengio, and G. J. Gordon, “An Empirical Study of Example Forgetting during Deep Neural Network Learning,” arXiv e-prints , p. arXiv:1812.05159, Dec 2018
Original
2018
Later among the works it cites.
A. Malinin and M. Gales, “Predictive Uncertainty Estimation via Prior Networks,” arXiv e-prints , p. arXiv:1802.10501, Feb 2018
Original
2018
Later among the works it cites.
K. Chitta, J. Feng, and M. Hebert, “Adaptive Semantic Segmentation with a Strategic Curriculum of Proxy Labels,” arXiv e-prints , p. arXiv:1811.03542, Nov 2018

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2019

Training Data Subset Search with Ensemble Active Learning

Chitta, Kashyap, Alvarez, Jose M., Haussmann, Elmar et al.

Understand

Deep Neural Networks (DNNs) often rely on very large datasets for training.

Given the large size of such datasets, it is conceivable that they contain certain samples that either do not contribute or negatively impact the DNN's optimization.
Modifying the training distribution in a way that excludes such samples could provide an effective solution to both improve performance and reduce training time.
In this paper, we propose to scale up ensemble Active Learning (AL) methods to perform acquisition at a large scale (10k to 500k samples at a time).

Built on

C. E. Shannon, “A mathematical theory of communication,” The Bell System Technical Journal , vol. 27, no. 3, pp. 379–423, 7 1948
1948
Earlier work this paper cites.
L. K. Hansen and P. Salamon, “Neural network ensembles,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 12, no. 10, pp. 993–1001, Oct. 1990
1990
Earlier work this paper cites.
D. Cohn, L. Atlas, and R. Ladner, “Improving generalization with active learning,” Mach. Learn. , vol. 15, no. 2, pp. 201–221, May 1994
1994
Earlier work this paper cites.
T. G. Dietterich, “Ensemble methods in machine learning,” in Multiple Classifier Systems, LBCS-1857 . Springer, 2000, pp. 1–15
2000
Earlier work this paper cites.
G. Schohn and D. Cohn, “Less is more: Active learning with support vector machines,” in ICML , 2000, pp. 839–846
2000
Earlier work this paper cites.
A. Krizhevsky, “Learning multiple layers of features from tiny images,” Tech. Rep., 2009
2009
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in CVPR , 2009
2009
Earlier work this paper cites.
B. Settles, “Active learning literature survey,” Tech. Rep., 2010
2010
Earlier work this paper cites.
M. Everingham, L. Gool, C. K. Williams, J. Winn, and A. Zisserman, “The pascal visual object classes (voc) challenge,” IJCV , vol. 88, no. 2, p. 303–338, Jun. 2010
2010
Earlier work this paper cites.
N. Houlsby, F. Huszár, Z. Ghahramani, and M. Lengyel, “Bayesian Active Learning for Classification and Preference Learning,” arXiv e-prints , p. arXiv:1112.5745, Dec 2011
Original
2011
Earlier work this paper cites.
A. Lapedriza, H. Pirsiavash, Z. Bylinskii, and A. Torralba, “Are all training examples equally valuable?” arXiv e-prints , Nov 2013
2013
Earlier work this paper cites.
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, “Microsoft coco: Common objects in context,” in ECCV , 2014
2014
Earlier work this paper cites.
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A simple way to prevent neural networks from overfitting,” in ICML , 2014
2014
Earlier work this paper cites.
Y. Gal and Z. Ghahramani, “Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning,” arXiv e-prints , p. arXiv:1506.02142, Jun 2015
Original
2015
Earlier work this paper cites.
Y. Gal and Z. Ghahramani, “Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference,” arXiv e-prints , Jun 2015
2015
Earlier work this paper cites.
E. Gavves, T. Mensink, T. Tommasi, C. G. M. Snoek, and T. Tuytelaars, “Active transfer learning with zero-shot priors: Reusing past datasets for future tasks,” in ICCV , Dec 2015, pp. 2731–2739
2015
Earlier work this paper cites.
K. Konyushkova, R. Sznitman, and P. Fua, “Introducing geometry in active learning for image segmentation,” in ICCV , 2015
2015
Earlier work this paper cites.
Y. Gal, “Uncertainty in deep learning,” Ph.D. dissertation, University of Cambridge, 2016
2016
Earlier work this paper cites.
W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg, “Ssd: Single shot multibox detector,” in ECCV , 2016
2016
Earlier work this paper cites.
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in CVPR , 2016
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in CVPR , 2016
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Identity Mappings in Deep Residual Networks,” arXiv e-prints , p. arXiv:1603.05027, Mar 2016
Original
2016
Earlier work this paper cites.

Similar

G. Huang, Z. Liu, L. van der Maaten, and K. Q. Weinberger, “Densely Connected Convolutional Networks,” arXiv e-prints , p. arXiv:1608.06993, Aug 2016
Original
2016
Cited alongside, same era.
D. Golovin, B. Solnik, S. Moitra, G. Kochanski, J. Karro, and D. Sculley, “Google vizier: A service for black-box optimization,” in SIGKDD , 2017
2017
Cited alongside, same era.
C. Sun, A. Shrivastava, S. Singh, and A. Gupta, “Revisiting Unreasonable Effectiveness of Data in Deep Learning Era,” arXiv e-prints , p. arXiv:1707.02968, Jul 2017
Original
2017
Cited alongside, same era.
B. Lakshminarayanan, A. Pritzel, and C. Blundell, “Simple and scalable predictive uncertainty estimation using deep ensembles,” in NIPS , 2017
2017
Cited alongside, same era.
Y. Gal, R. Islam, and Z. Ghahramani, “Deep Bayesian Active Learning with Image Data,” arXiv e-prints , p. arXiv:1703.02910, Mar 2017

Then

S. Roy, A. Unmesh, and V. P. Namboodiri, “Deep active learning for object detection,” in BMVC , 2018
2018
Later among the works it cites.
K. Vodrahalli, K. Li, and J. Malik, “Are All Training Examples Created Equal? An Empirical Study,” arXiv e-prints , p. arXiv:1811.12569, Nov 2018
Original
2018
Later among the works it cites.
M. Toneva, A. Sordoni, R. Tachet des Combes, A. Trischler, Y. Bengio, and G. J. Gordon, “An Empirical Study of Example Forgetting during Deep Neural Network Learning,” arXiv e-prints , p. arXiv:1812.05159, Dec 2018
Original
2018
Later among the works it cites.
A. Malinin and M. Gales, “Predictive Uncertainty Estimation via Prior Networks,” arXiv e-prints , p. arXiv:1802.10501, Feb 2018
Original
2018
Later among the works it cites.
K. Chitta, J. Feng, and M. Hebert, “Adaptive Semantic Segmentation with a Strategic Curriculum of Proxy Labels,” arXiv e-prints , p. arXiv:1811.03542, Nov 2018

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Original

2017

Cited alongside, same era.

O. Sener and S. Savarese, “Active Learning for Convolutional Neural Networks: A Core-Set Approach,” arXiv e-prints , p. arXiv:1708.00489, Aug 2017

Original

2017

Cited alongside, same era.

H.-S. Chang, E. Learned-Miller, and A. McCallum, “Active Bias: Training More Accurate Neural Networks by Emphasizing High Variance Samples,” arXiv e-prints , p. arXiv:1704.07433, Apr 2017

Original

2017

Cited alongside, same era.

A. Kendall and Y. Gal, “What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?” arXiv e-prints , p. arXiv:1703.04977, Mar 2017

Original

2017

Cited alongside, same era.

S. Depeweg, J. M. Hernández-Lobato, F. Doshi-Velez, and S. Udluft, “Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning,” arXiv e-prints , p. arXiv:1710.07283, Oct 2017

Original

2017

Cited alongside, same era.

G. Huang, Y. Li, G. Pleiss, Z. Liu, J. E. Hopcroft, and K. Q. Weinberger, “Snapshot Ensembles: Train 1, get M for free,” arXiv e-prints , p. arXiv:1704.00109, Mar 2017

Original

2017

Cited alongside, same era.

C. Wong, N. Houlsby, Y. Lu, and A. Gesmundo, “Transfer learning with neural automl,” in NIPS , 2018, pp. 8356–8365

2018

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Y. He, J. Lin, Z. Liu, H. Wang, L.-J. Li, and S. Han, “AMC: AutoML for Model Compression and Acceleration on Mobile Devices,” arXiv e-prints , p. arXiv:1802.03494, Feb 2018

Original

2018

Cited alongside, same era.

Original

2018

Later among the works it cites.

R. Pop and P. Fulop, “Deep Ensemble Bayesian Active Learning : Addressing the Mode Collapse issue in Monte Carlo dropout via Ensembles,” arXiv e-prints , p. arXiv:1811.03897, Nov 2018

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2018

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R. Schwartz, J. Dodge, N. A. Smith, and O. Etzioni, “Green AI,” arXiv e-prints , p. arXiv:1907.10597, Jul 2019

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2018

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X. He, K. Zhao, and X. Chu, “AutoML: A Survey of the State-of-the-Art,” arXiv e-prints , p. arXiv:1908.00709, Aug 2019

Original

2019

Closest in time.

S. Xie, H. Zheng, C. Liu, and L. Lin, “SNAS: stochastic neural architecture search,” in ICLR , 2019

2019

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X. Dong and Y. Yang, “Searching for a robust neural architecture in four gpu hours,” in CVPR , June 2019

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K. Kandasamy, K. Raju Vysyaraju, W. Neiswanger, B. Paria, C. R. Collins, J. Schneider, B. Poczos, and E. P. Xing, “Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly,” arXiv e-prints , p. arXiv:1903.06694, Mar 2019

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2019

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I. Zeki Yalniz, H. Jégou, K. Chen, M. Paluri, and D. Mahajan, “Billion-scale semi-supervised learning for image classification,” arXiv e-prints , p. arXiv:1905.00546, May 2019

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2019

Closest in time.

Y. Ovadia, E. Fertig, J. Ren, Z. Nado, D. Sculley, S. Nowozin, J. V. Dillon, B. Lakshminarayanan, and J. Snoek, “Can You Trust Your Model’s Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift,” arXiv e-prints , p. arXiv:1906.02530, Jun 2019

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2019

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S. Sinha, S. Ebrahimi, and T. Darrell, “Variational Adversarial Active Learning,” arXiv e-prints , p. arXiv:1904.00370, Mar 2019

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C.-A. Brust, C. Käding, and J. Denzler, “Active learning for deep object detection,” in VISAPP , 2019

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S. V. Desai, A. C. Lagandula, W. Guo, S. Ninomiya, and V. N. Balasubramanian, “An adaptive supervision framework for active learning in object detection,” in BMVC , 2019

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H. H. Aghdam, A. Gonzalez-Garcia, J. van de Weijer, and A. M. Lopez, “Active learning for deep detection neural networks,” in ICCV , 2019

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V. Birodkar, H. Mobahi, and S. Bengio, “Semantic Redundancies in Image-Classification Datasets: The 10% You Don’t Need,” arXiv e-prints , p. arXiv:1901.11409, Jan 2019

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C. Coleman, C. Yeh, S. Mussmann, B. Mirzasoleiman, P. Bailis, P. Liang, J. Leskovec, and M. Zaharia, “Selection via Proxy: Efficient Data Selection for Deep Learning,” arXiv e-prints , p. arXiv:1906.11829, Jun. 2019

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