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Even as deep neural networks (DNNs) have achieved remarkable success on vision-related tasks, their performance is brittle to transformations in the input.
On the efficiency of certain quasi-random sequences of points in evaluating multi-dimensional integrals
J. H. Halton · 1960
Earlier work this paper cites.
Geometric Modeling for Computer Vision
Bruce Guenther Baumgart · 1974
Earlier work this paper cites.
Induction of decision trees
J. R. Quinlan · 1986
Earlier work this paper cites.
A mathematical theory of communication
C. E. Shannon · 2001
Earlier work this paper cites.
Automatic lighting design using a perceptual quality metric
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Earlier work this paper cites.
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Volker Blanz and Thomas Vetter · 2002
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.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Angel X Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, et al · 2015
Earlier work this paper cites.
Shape, illumination, and reflectance from shading
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Earlier work this paper cites.
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