Fetching the paper…
Reading the bibliography…
We tackle the problem of Selective Classification where the objective is to achieve the best performance on a predetermined ratio (coverage) of the dataset.
On optimum recognition error and reject tradeoff
C. Chow · 1970
Earlier work this paper cites.
The nearest neighbor classification rule with a reject option
Martin E. Hellman · 1970
Earlier work this paper cites.
Handwritten digit recognition with a back-propagation network
Yann LeCun, Bernhard Boser, John Denker, Donnie Henderson, Richard Howard, Wayne Hubbard, and Lawrence Jackel · 1989
Earlier work this paper cites.
Support vector machines with embedded reject option
Giorgio Fumera and Fabio Roli · 2002
Earlier work this paper cites.
Semi-supervised learning by entropy minimization
Yves Grandvalet and Yoshua Bengio · 2004
Earlier work this paper cites.
Lasso type classifiers with a reject option
Marten Wegkamp · 2007
Earlier work this paper cites.
Classification with a reject option using a hinge loss
Peter L. Bartlett and Marten H. Wegkamp · 2008
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
Earlier work this paper cites.
Support vector machines with a reject option
Marten Wegkamp and Ming Yuan · 2011
Earlier work this paper cites.
3d object representations for fine-grained categorization
Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei · 2013
Earlier work this paper cites.
Food-101 – mining discriminative components with random forests
Lukas Bossard, Matthieu Guillaumin, and Luc Van Gool · 2014
Earlier work this paper cites.
Boosting with abstention
Corinna Cortes, Giulia DeSalvo, and Mehryar Mohri · 2016
Cited alongside, same era.
Dropout as a Bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani · 2016
Cited alongside, same era.
A baseline for detecting misclassified and out-of-distribution examples in neural networks
Dan Hendrycks and Kevin Gimpel · 2016
Cited alongside, same era.
Unsupervised domain adaptation with residual transfer networks
Mingsheng Long, Han Zhu, Jianmin Wang, and Michael I Jordan · 2016
Cited alongside, same era.
Selective classification for deep neural networks
Yonatan Geifman and Ran El-Yaniv · 2017
Cited alongside, same era.
On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 2017
Efficient and scalable Bayesian neural nets with rank-1 factors
Michael Dusenberry, Ghassen Jerfel, Yeming Wen, Yian Ma, Jasper Snoek, Katherine Heller, Balaji Lakshminarayanan, and Dustin Tran · 2020
Later among the works it cites.
Self-adaptive training: beyond empirical risk minimization
Lang Huang, Chao Zhang, and Hongyang Zhang · 2020
Later among the works it cites.
Consistent estimators for learning to defer to an expert
Hussein Mozannar and David Sontag · 2020
Later among the works it cites.
Contrastive multiview coding
Yonglong Tian, Dilip Krishnan, and Phillip Isola · 2020
Later among the works it cites.
Entropy minimization vs. diversity maximization for domain adaptation
Xiaofu Wu, Quan Zhou, Zhen Yang, Chunming Zhao, Longin Jan Latecki, et al · 2020
Later among the works it cites.
Classification with rejection based on cost-sensitive classification
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
Cited alongside, same era.
Selectivenet: A deep neural network with an integrated reject option
Yonatan Geifman and Ran El-Yaniv · 2019
Cited alongside, same era.
A simple baseline for bayesian uncertainty in deep learning
Wesley J Maddox, Pavel Izmailov, Timur Garipov, Dmitry P Vetrov, and Andrew Gordon Wilson · 2019
Cited alongside, same era.
Advent: Adversarial entropy minimization for domain adaptation in semantic segmentation
Tuan-Hung Vu, Himalaya Jain, Maxime Bucher, Matthieu Cord, and Patrick Pérez · 2019
Cited alongside, same era.
Deep gamblers: learning to abstain with portfolio theory
Liu Ziyin, Zhikang T Wang, Paul Pu Liang, Ruslan Salakhutdinov, Louis-Philippe Morency, and Masahito Ueda · 2019
Cited alongside, same era.
Nontawat Charoenphakdee, Zhenghang Cui, Yivan Zhang, and Masashi Sugiyama · 2021
Later among the works it cites.
Selective classification can magnify disparities across groups
Erik Jones, Shiori Sagawa, Pang Wei Koh, Ananya Kumar, and Percy Liang · 2021
Later among the works it cites.
Fair selective classification via sufficiency
Joshua K Lee, Yuheng Bu, Deepta Rajan, Prasanna Sattigeri, Rameswar Panda, Subhro Das, and Gregory W Wornell · 2021
Later among the works it cites.
Revisiting the calibration of modern neural networks
Matthias Minderer, Josip Djolonga, Rob Romijnders, Frances Hubis, Xiaohua Zhai, Neil Houlsby, Dustin Tran, and Mario Lucic · 2021
Later among the works it cites.
The familiarity hypothesis: Explaining the behavior of deep open set methods
Thomas G Dietterich and Alex Guyer · 2022
Closest in time.
Self-adaptive training: Bridging supervised and self-supervised learning
Lang Huang, Chao Zhang, and Hongyang Zhang · 2022
Closest in time.