Learning From Noisy Singly-labeled Data

Maximum likelihood estimation of observer error-rates using the em algorithm

Alexander Philip Dawid and Allan M Skene · 1979

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Learning with multiple labels

Rong Jin and Zoubin Ghahramani · 2003

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Get another label? improving data quality and data mining using multiple, noisy labelers

Victor S Sheng, Foster Provost, and Panagiotis G Ipeirotis · 2008

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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 and Geoffrey Hinton · 2009

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Whose vote should count more: Optimal integration of labels from labelers of unknown expertise

Jacob Whitehill, Ting-fan Wu, Jacob Bergsma, Javier R Movellan, and Paul L Ruvolo · 2009

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Visual recognition with humans in the loop

Steve Branson, Catherine Wah, Florian Schroff, Boris Babenko, Peter Welinder, Pietro Perona, and Serge Belongie · 2010

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Online crowdsourcing: rating annotators and obtaining cost-effective labels

Peter Welinder and Pietro Perona · 2010

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The multidimensional wisdom of crowds

Peter Welinder, Steve Branson, Pietro Perona, and Serge J Belongie · 2010

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Multiclass recognition and part localization with humans in the loop

Catherine Wah, Steve Branson, Pietro Perona, and Serge Belongie · 2011

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Variational inference for crowdsourcing

Qiang Liu, Jian Peng, and Alexander T Ihler · 2012

Cited alongside, same era.

Learning from the wisdom of crowds by minimax entropy

Original

Denny Zhou, Sumit Basu, Yi Mao, and John C Platt · 2012

Cited alongside, same era.

Aggregating crowdsourced binary ratings

Nilesh Dalvi, Anirban Dasgupta, Ravi Kumar, and Vibhor Rastogi · 2013

Cited alongside, same era.

Fine-grained crowdsourcing for fine-grained recognition

Jia Deng, Jonathan Krause, and Li Fei-Fei · 2013

Cited alongside, same era.

Learning with noisy labels

Nagarajan Natarajan, Inderjit S Dhillon, Pradeep K Ravikumar, and Ambuj Tewari · 2013

Cited alongside, same era.

Repeated labeling using multiple noisy labelers

Panagiotis G Ipeirotis, Foster Provost, Victor S Sheng, and Jing Wang · 2014

Cited alongside, same era.

Optimal testing for crowd workers

Jonathan Bragg, Daniel S Weld, et al · 2016

Later among the works it cites.

Learning deep networks from noisy labels with dropout regularization

Ishan Jindal, Matthew Nokleby, and Xuewen Chen · 2016

Later among the works it cites.

Learning visual features from large weakly supervised data

Armand Joulin, Laurens van der Maaten, Allan Jabri, and Nicolas Vasilache · 2016

Later among the works it cites.

Achieving budget-optimality with adaptive schemes in crowdsourcing

Ashish Khetan and Sewoong Oh · 2016

Later among the works it cites.

The unreasonable effectiveness of noisy data for fine-grained recognition

Jonathan Krause, Benjamin Sapp, Andrew Howard, Howard Zhou, Alexander Toshev, Tom Duerig, James Philbin, and Li Fei-Fei · 2016

Later among the works it cites.

Re-active learning: Active learning with relabeling

Christopher H Lin, M Mausam, and Daniel S Weld · 2016