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Tractable models of human perception have proved to be challenging to build.
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Zhou Wang, Eero. Simoncelli and Alan. Bovik · 2003
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Alex Berardino, Johannes Ballé, Valero Laparra and Eero. Simoncelli · 2017
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“Detecting the psychovisual impact of compression related artifacts using SSIMULACRA”, 2017
Jon Sneyers · 2017
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Troy Chinen et al · 2018
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“Representation Learning with Contrastive Predictive Coding”, 2018
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Felix Creutzig and Henning Sprekeler · 2008
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Arpad. Elo · 2008
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Zhou Wang and Eero. Simoncelli · 2008
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Lin Zhang, Lei Zhang, Xuanqin Mou and David Zhang · 2011
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“ImageNet Classification with Deep Convolutional Neural Networks”
Alex Krizhevsky, Ilya Sutskever and Geoffrey. Hinton · 2012
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Diederik. Kingma and Jimmy Ba · 2015
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“ImageNet Large Scale Visual Recognition Challenge”
Olga Russakovsky et al · 2015
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Aäron van Oord, Yazhe Li and Oriol Vinyals · 2018
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“The Unreasonable Effectiveness of Deep Features as a Perceptual Metric”
Richard Zhang et al · 2018
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URL: https://opensource.google/projects/butteraugli
“Butteraugli”, 2019 · 2019
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“Bounding the Multivariate Mutual Information”
Ian Fischer · 2019
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“The Conditional Entropy Bottleneck”
Ian Fischer · 2019
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“Benchmarking Neural Network Robustness to Common Corruptions and Perturbations”
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“On Variational Bounds of Mutual Information”
Ben Poole et al · 2019
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URL: http://www.compression.cc/2020/
“CLIC: Workshop and Challenge on Learned Image Compression”, 2020 · 2020
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“From Patches to Pictures (PaQ-2-PiQ): Mapping the Perceptual Space of Picture Quality” To appear
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