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Current discriminative depth estimation methods often produce blurry artifacts, while generative approaches suffer from slow sampling due to curvatures in the noise-to-depth transport.
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
Sanh, V.; Debut, L.; Chaumond, J.; and Wolf, T. 2020 · 1910
Earlier work this paper cites.
Learning from noisy labels with distillation
Li, Y.; Yang, J.; Song, Y.; Cao, L.; Luo, J.; and Li, L.-J. 2017 · 1918
Earlier work this paper cites.
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Geiger, A.; Lenz, P.; and Urtasun, R. 2012 · 2012
Earlier work this paper cites.
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Nathan Silberman, P. K., Derek Hoiem; and Fergus, R. 2012 · 2012
Earlier work this paper cites.
Vision meets robotics: The kitti dataset
Geiger, A.; Lenz, P.; Stiller, C.; and Urtasun, R. 2013 · 2013
Earlier work this paper cites.
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Scharstein, D.; Hirschmüller, H.; Kitajima, Y.; Krathwohl, G.; Nešić, N.; Wang, X.; and Westling, P. 2014 · 2014
Earlier work this paper cites.
Distilling the knowledge in a neural network
Hinton, G.; Vinyals, O.; and Dean, J. 2015 · 2015
Earlier work this paper cites.
Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J.; Weiss, E.; Maheswaranathan, N.; and Ganguli, S. 2015 · 2015
Earlier work this paper cites.
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Song, S.; Lichtenberg, S. P.; and Xiao, J. 2015 · 2015
Earlier work this paper cites.
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Earlier work this paper cites.
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Schops, T.; Schonberger, J. L.; Galliani, S.; Sattler, T.; Schindler, K.; Pollefeys, M.; and Geiger, A. 2017 · 2017
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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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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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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Earlier work this paper cites.
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Later among the works it cites.
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