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Generative models often use human evaluations to measure the perceived quality of their outputs.
The staircrase-method in psychophysics
Tom N Cornsweet · 1962
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A model for visual memory tasks
George Sperling · 1963
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Transformed up-down methods in psychoacoustics
HCCH Levitt · 1971
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Perception and estimation of time
Paul Fraisse · 1984
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Confidence limits on phylogenies: an approach using the bootstrap
Joseph Felsenstein · 1985
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Sustained work, fatigue, sleep loss and performance: A review of the issues
Gerald P Krueger · 1989
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Advances in automatic text summarization
Inderjeet Mani · 1999
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A parametric texture model based on joint statistics of complex wavelet coefficients
Javier Portilla and Eero P Simoncelli · 2000
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Measuring, estimating, and understanding the psychometric function: A commentary
Stanley A Klein · 2001
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The psychometric function: I. fitting, sampling, and goodness of fit
Felix A Wichmann and N Jeremy Hill · 2001
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Training products of experts by minimizing contrastive divergence
Geoffrey E Hinton · 2002
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Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu · 2002
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Meteor: An automatic metric for mt evaluation with improved correlation with human judgments
Satanjeev Banerjee and Alon Lavie · 2005
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Pattern recognition and machine learning
Christopher M Bishop · 2006
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What do we perceive in a glance of a real-world scene?
Li Fei-Fei, Asha Iyer, Christof Koch, and Pietro Perona · 2007
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Crowdsourcing user studies with mechanical turk
Aniket Kittur, Ed H Chi, and Bongwon Suh · 2008
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Psychophysical evidence for a non-linear representation of facial identity
Steven C Dakin and Diana Omigie · 2009
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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The briefest of glances: The time course of natural scene understanding
Michelle R Greene and Aude Oliva · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
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Eye movements and visual encoding during scene perception
Keith Rayner, Tim J Smith, George L Malcolm, and John M Henderson · 2009
Cited alongside, same era.
Face recognition by computers and humans
Rama Chellappa, Pawan Sinha, and P Jonathon Phillips · 2010
Cited alongside, same era.
Ensuring quality in crowdsourced search relevance evaluation: The effects of training question distribution
John Le, Andy Edmonds, Vaughn Hester, and Lukas Biewald · 2010
Cited alongside, same era.
Crowds in two seconds: Enabling realtime crowd-powered interfaces
Michael S Bernstein, Joel Brandt, Robert C Miller, and David R Karger · 2011
Cited alongside, same era.
Inserting micro-breaks into crowdsourcing workflows
Jeffrey M Rzeszotarski, Ed Chi, Praveen Paritosh, and Peng Dai · 2013
Cited alongside, same era.
Neural machine translation by jointly learning to align and translate
Improving generative adversarial networks with denoising feature matching
David Warde-Farley and Yoshua Bengio · 2016
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Began: boundary equilibrium generative adversarial networks
David Berthelot, Thomas Schumm, and Luke Metz · 2017
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Improved training of wasserstein gans
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Courville · 2017
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A glimpse far into the future: Understanding long-term crowd worker quality
Kenji Hata, Ranjay Krishna, Li Fei-Fei, and Michael S Bernstein · 2017
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
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Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2014
Cited alongside, same era.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Cited alongside, same era.
Deep generative image models using a laplacian pyramid of adversarial networks
Emily L Denton, Soumith Chintala, Rob Fergus, et al · 2015
Cited alongside, same era.
Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
Cited alongside, same era.
Comparing person-and process-centric strategies for obtaining quality data on amazon mechanical turk
Tanushree Mitra, Clayton J Hutto, and Eric Gilbert · 2015
Cited alongside, same era.
Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 2015
Cited alongside, same era.
A note on the evaluation of generative models
Lucas Theis, Aäron van den Oord, and Matthias Bethge · 2015
Cited alongside, same era.
Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen · 2017
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Dense-captioning events in videos
Ranjay Krishna, Kenji Hata, Frederic Ren, Li Fei-Fei, and Juan Carlos Niebles · 2017
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Variational approaches for auto-encoding generative adversarial networks
Mihaela Rosca, Balaji Lakshminarayanan, David Warde-Farley, and Shakir Mohamed · 2017
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Shane Barratt and Rishi Sharma · 2018
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Mikołaj Bińkowski, Dougal J Sutherland, Michael Arbel, and Arthur Gretton · 2018
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Pros and cons of gan evaluation measures
Ali Borji · 2018
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Large scale gan training for high fidelity natural image synthesis
Andrew Brock, Jeff Donahue, and Karen Simonyan · 2018
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A style-based generator architecture for generative adversarial networks
Tero Karras, Samuli Laine, and Timo Aila · 2018
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Are gans created equal? a large-scale study
Mario Lucic, Karol Kurach, Marcin Michalski, Sylvain Gelly, and Olivier Bousquet · 2018
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Spectral normalization for generative adversarial networks
Takeru Miyato, Toshiki Kataoka, Masanori Koyama, and Yuichi Yoshida · 2018
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Skill rating for generative models
Catherine Olsson, Surya Bhupatiraju, Tom Brown, Augustus Odena, and Ian Goodfellow · 2018
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Learning implicit generative models with the method of learned moments
Suman Ravuri, Shakir Mohamed, Mihaela Rosca, and Oriol Vinyals · 2018
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Assessing generative models via precision and recall
Mehdi SM Sajjadi, Olivier Bachem, Mario Lucic, Olivier Bousquet, and Sylvain Gelly · 2018
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Unifying human and statistical evaluation for natural language generation
Tatsunori B Hashimoto, Hugh Zhang, and Percy Liang · 2019
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Faceforensics++: Learning to detect manipulated facial images
Andreas Rössler, Davide Cozzolino, Luisa Verdoliva, Christian Riess, Justus Thies, and Matthias Nießner · 2019
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