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Post-hoc multi-class calibration is a common approach for providing high-quality confidence estimates of deep neural network predictions.
Probabilities for SV machines
John Platt · 1999
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The information bottleneck method
Naftali Tishby, Fernando C. Pereira, and William Bialek · 1999
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Obtaining calibrated probability estimates from decision trees and naive bayesian classifiers
Bianca Zadrozny and Charles Elkan · 2001
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Transforming classifier scores into accurate multiclass probability estimates
Bianca Zadrozny and Charles Elkan · 2002
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k-means++: the advantages of careful seeding
David Arthur and Sergei Vassilvitskii · 2007
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ImageNet: A Large-Scale Hierarchical Image Database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y. Ng · 2011
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Weight uncertainty in neural networks
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra · 2015
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Obtaining well calibrated probabilities using Bayesian binning
Mahdi Pakdaman Naeini, Gregory F. Cooper, and Milos Hauskrecht · 2015
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Wide residual networks
Sergey Zagoruyko and Nikos Komodakis · 2016
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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q. Weinberger · 2017
Cited alongside, same era.
Densely connected convolutional networks
G. Huang, Z. Liu, L. v. d. Maaten, and K. Q. Weinberger · 2017
Cited alongside, same era.
Beta calibration: a well-founded and easily implemented improvement on logistic calibration for binary classifiers
Meelis Kull, Telmo Silva Filho, and Peter Flach · 2017
Cited alongside, same era.
Regularizing neural networks by penalizing confident output distributions
Gabriel Pereyra, George Tucker, Jan Chorowski, Łukasz Kaiser, and Geoffrey Hinton · 2017
Cited alongside, same era.
Inception-v4, Inception-ResNet and the impact of residual connections on learning
Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, and Alexander A. Alemi · 2017
Cited alongside, same era.
Aggregated residual transformations for deep neural networks
On-manifold adversarial data augmentation improves uncertainty calibration
Kanil Patel, William Beluch, Dan Zhang, Michael Pfeiffer, and Bin Yang · 2019
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Sampling-free epistemic uncertainty estimation using approximated variance propagation
Janis Postels, Francesco Ferroni, Huseyin Coskun, Nassir Navab, and Federico Tombari · 2019
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Anchor loss: Modulating loss scale based on prediction difficulty
Serim Ryou, Seong-Gyun Jeong, and Pietro Perona · 2019
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On mixup training: Improved calibration and predictive uncertainty for deep neural networks
Sunil Thulasidasan, Gopinath Chennupati, Jeff Bilmes, Tanmoy Bhattacharya, and Sarah Michalak · 2019
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Evaluating model calibration in classification
Juozas Vaicenavicius, David Widmann, Carl Andersson, Fredrik Lindsten, Jacob Roll, and Thomas Schön · 2019
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Saining Xie, Ross B. Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He · 2017
Cited alongside, same era.
Trainable calibration measures for neural networks from kernel mean embeddings
Aviral Kumar, Sunita Sarawagi, and Ujjwal Jain · 2018
Cited alongside, same era.
Dirichlet-based gaussian processes for large-scale calibrated classification
Dimitrios Milios, Raffaello Camoriano, Pietro Michiardi, Lorenzo Rosasco, and Maurizio Filippone · 2018
Cited alongside, same era.
mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cissé, Yann Dauphin, and David Lopez-Paz · 2018
Cited alongside, same era.
Bin-wise temperature scaling (BTS): Improvement in confidence calibration performance through simple scaling techniques
B. Ji, H. Jung, J. Yoon, K. Kim, and y. Shin · 2019
Cited alongside, same era.
Beyond temperature scaling: Obtaining well-calibrated multi-class probabilities with dirichlet calibration
Meelis Kull, Miquel Perello Nieto, Markus Kängsepp, Telmo Silva Filho, Hao Song, and Peter Flach · 2019
Cited alongside, same era.
Verified uncertainty calibration
Ananya Kumar, Percy S Liang, and Tengyu Ma · 2019
Cited alongside, same era.
Calibration tests in multi-class classification: A unifying framework
David Widmann, Fredrik Lindsten, and Dave Zachariah · 2019
Later among the works it cites.
Cutmix: Regularization strategy to train strong classifiers with localizable features
Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, and Youngjoon Yoo · 2019
Later among the works it cites.
Pitfalls of in-domain uncertainty estimation and ensembling in deep learning
Arsenii Ashukha, Alexander Lyzhov, Dmitry Molchanov, and Dmitry Vetrov · 2020
Closest in time.
Augmix: A simple method to improve robustness and uncertainty under data shift
Dan Hendrycks, Norman Mu, Ekin Dogus Cubuk, Barret Zoph, Justin Gilmer, and Balaji Lakshminarayanan · 2020
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Ensemble distribution distillation
Andrey Malinin, Bruno Mlodozeniec, and Mark Gales · 2020
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Non-parametric calibration for classification
Jonathan Wenger, Hedvig Kjellström, and Rudolph Triebel · 2020
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Mix-n-Match: Ensemble and compositional methods for uncertainty calibration in deep learning
Jize Zhang, Bhavya Kailkhura, and T Han · 2020
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