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Many real-world vision problems suffer from inherent ambiguities.
Diversity and dissimilarity coefficients: a unified approach
Rao, C.R.: · 1982
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A proof of the triangle inequality for the tanimoto distance
Lipkus, A.H.: · 1999
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N-distances and their applications
Klebanov, L.B., Beneš, V., Saxl, I.: · 2005
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The lung image database consortium (lidc) and image database resource initiative (idri): a completed reference database of lung nodules on ct scans
Armato, S.G., McLennan, G., Bidaut, L., McNitt-Gray, M.F., Meyer, C.R., Reeves, A.P., Zhao, B., Aberle, D.R., Henschke, C.I., Hoffman, E.A., et al.: · 2011
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Multiple choice learning: Learning to produce multiple structured outputs
Guzman-Rivera, A., Batra, D., Kohli, P.: · 2012
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Diverse m-best solutions in markov random fields
Batra, D., Yadollahpour, P., Guzman-Rivera, A., Shakhnarovich, G.: · 2012
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Auto-encoding variational bayes
Kingma, D.P., Welling, M.: · 2013
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Computing the m most probable modes of a graphical model
Chen, C., Kolmogorov, V., Zhu, Y., Metaxas, D., Lampert, C.: · 2013
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Energy statistics: A class of statistics based on distances
Székely, G.J., Rizzo, M.L.: · 2013
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The cancer imaging archive (tcia): maintaining and operating a public information repository
Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., et al.: · 2013
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Stochastic backpropagation and approximate inference in deep generative models
Jimenez Rezende, D., Mohamed, S., Wierstra, D.: · 2014
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Semi-supervised learning with deep generative models
Kingma, D.P., Jimenez Rezende, D., Mohamed, S., Welling, M.: · 2014
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Adam: A method for stochastic optimization
Kingma, D.P., Ba, J.: · 2014
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Learning structured output representation using deep conditional generative models
Sohn, K., Lee, H., Yan, X.: · 2015
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U-net: Convolutional networks for biomedical image segmentation
Ronneberger, O., Fischer, P., Brox, T.: · 2015
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Kendall, A., Badrinarayanan, V., Cipolla, R.: · 2015
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Why m heads are better than one: Training a diverse ensemble of deep networks
Lee, S., Purushwalkam, S., Cogswell, M., Crandall, D., Batra, D.: · 2015
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Inferring m-best diverse labelings in a single one
Kirillov, A., Savchynskyy, B., Schlesinger, D., Vetrov, D., Rother, C.: · 2015
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The cityscapes dataset for semantic urban scene understanding
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., Schiele, B.: · 2016
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Improved techniques for training gans
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., Chen, X.: · 2016
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What uncertainties do we need in bayesian deep learning for computer vision?
Kendall, A., Gal, Y.: · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
Lakshminarayanan, B., Pritzel, A., Blundell, C.: · 2017
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Learning in an uncertain world: Representing ambiguity through multiple hypotheses
Rupprecht, C., Laina, I., DiPietro, R., Baust, M., Tombari, F., Navab, N., Hager, G.D.: · 2017
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Image-to-image translation with conditional adversarial networks
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M-best-diverse labelings for submodular energies and beyond
Kirillov, A., Shlezinger, D., Vetrov, D.P., Rother, C., Savchynskyy, B.: · 2015
Cited alongside, same era.
Data from lidc-idri. the cancer imaging archive
Armato, I., Samuel, G., McLennan, G., Bidaut, L., McNitt-Gray, M.F., Meyer, C.R., Reeves, A.P., Clarke, L.P.: · 2015
Cited alongside, same era.
Stochastic multiple choice learning for training diverse deep ensembles
Lee, S., Prakash, S.P.S., Cogswell, M., Ranjan, V., Crandall, D., Batra, D.: · 2016
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Joint m-best-diverse labelings as a parametric submodular minimization
Kirillov, A., Shekhovtsov, A., Rother, C., Savchynskyy, B.: · 2016
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Nips 2016 tutorial: Generative adversarial networks
Goodfellow, I.: · 2016
Cited alongside, same era.
Disco nets: Dissimilarity coefficients networks
Bouchacourt, D., Mudigonda, P.K., Nowozin, S.: · 2016
Cited alongside, same era.
A note on the triangle inequality for the jaccard distance
Kosub, S.: · 2016
Cited alongside, same era.
Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: · 2017
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Toward multimodal image-to-image translation
Zhu, J.Y., Zhang, R., Pathak, D., Darrell, T., Efros, A.A., Wang, O., Shechtman, E.: · 2017
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beta-vae: Learning basic visual concepts with a constrained variational framework
Higgins, I., Matthey, L., Pal, A., Burgess, C., Glorot, X., Botvinick, M., Mohamed, S., Lerchner, A.: · 2017
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The cramer distance as a solution to biased wasserstein gradients
Bellemare, M.G., Danihelka, I., Dabney, W., Mohamed, S., Lakshminarayanan, B., Hoyer, S., Munos, R.: · 2017
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Uncertainty estimates for optical flow with multi-hypotheses networks
Ilg, E., Çiçek, Ö., Galesso, S., Klein, A., Makansi, O., Hutter, F., Brox, T.: · 2018
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A variational u-net for conditional appearance and shape generation
Esser, P., Sutter, E., Ommer, B.: · 2018
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Improving gans using optimal transport
Salimans, T., Zhang, H., Radford, A., Metaxas, D.: · 2018
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