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Devising domain- and model-agnostic evaluation metrics for generative models is an important and as yet unresolved problem.
Minimum volume sets and generalized quantile processes
Polonik, W · 1997
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The mnist database of handwritten digits
LeCun, Y · 1998
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Estimating the support of a high-dimensional distribution
Schölkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., and Williamson, R. C · 2001
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Training products of experts by minimizing contrastive divergence
Hinton, G. E · 2002
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Detection, estimation, and modulation theory, part I: detection, estimation, and linear modulation theory
Van Trees, H. L · 2004
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Learning minimum volume sets
Scott, C. D. and Nowak, R. D · 2006
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Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
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Estimation of non-normalized statistical models
Hyvärinen, A., Hurri, J., and Hoyer, P. O · 2009
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Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
Gutmann, M. and Hyvärinen, A · 2010
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New method for parameter estimation in probabilistic models: minimum probability flow
Sohl-Dickstein, J., Battaglino, P. B., and DeWeese, M. R · 2011
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Optimal kernel choice for large-scale two-sample tests
Gretton, A., Sejdinovic, D., Strathmann, H., Balakrishnan, S., Pontil, M., Fukumizu, K., and Sriperumbudur, B. K · 2012
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Better mixing via deep representations
Bengio, Y., Mesnil, G., Dauphin, Y., and Rifai, S · 2013
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2013
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
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Precision-recall-gain curves: Pr analysis done right
Flach, P. and Kull, M · 2015
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Unsupervised learning of video representations using lstms
Srivastava, N., Mansimov, E., and Salakhudinov, R · 2015
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A note on the evaluation of generative models
Theis, L., Oord, A. v. d., and Bethge, M · 2015
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Improved techniques for training gans
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., and Chen, X · 2016
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Generative models and model criticism via optimized maximum mean discrepancy
Assessing generative models via precision and recall
Sajjadi, M. S., Bachem, O., Lucic, M., Bousquet, O., and Gelly, S · 2018
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High-resolution image synthesis and semantic manipulation with conditional gans
Wang, T.-C., Liu, M.-Y., Zhu, J.-Y., Tao, A., Kautz, J., and Catanzaro, B · 2018
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Differentially private generative adversarial network
Xie, L., Lin, K., Wang, S., Wang, F., and Zhou, J · 2018
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Investigating under and overfitting in wasserstein generative adversarial networks
Adlam, B., Weill, C., and Kapoor, A · 2019
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Improved precision and recall metric for assessing generative models
Kynkäänniemi, T., Karras, T., Laine, S., Lehtinen, J., and Aila, T · 2019
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Sutherland, D. J., Tung, H.-Y., Strathmann, H., De, S., Ramdas, A., Smola, A., and Gretton, A · 2016
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Arjovsky, M., Chintala, S., and Bottou, L · 2017
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Improved training of wasserstein gans
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A · 2017
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Gans trained by a two time-scale update rule converge to a nash equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Klambauer, G., and Hochreiter, S · 2017
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Automatic differentiation in pytorch
Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., and Lerer, A · 2017
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Large scale gan training for high fidelity natural image synthesis
Brock, A., Donahue, J., and Simonyan, K · 2018
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Are gans created equal? a large-scale study
Lucic, M., Kurach, K., Michalski, M., Gelly, S., and Bousquet, O · 2018
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Ethnic and regional variations in hospital mortality from covid-19 in brazil: a cross-sectional observational study
Baqui, P., Bica, I., Marra, V., Ercole, A., and van Der Schaar, M · 2020
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Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
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Hide-and-seek privacy challenge
Jordon, J., Jarrett, D., Yoon, J., Barnes, T., Elbers, P., Thoral, P., Ercole, A., Zhang, C., Belgrave, D., and van der Schaar, M · 2020
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Training generative adversarial networks with limited data
Karras, T., Aittala, M., Hellsten, J., Laine, S., Lehtinen, J., and Aila, T · 2020
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A non-parametric test to detect data-copying in generative models
Meehan, C., Chaudhuri, K., and Dasgupta, S · 2020
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Reliable fidelity and diversity metrics for generative models
Naeem, M. F., Oh, S. J., Uh, Y., Choi, Y., and Yoo, J · 2020
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http://plataforma.saude.gov.br/coronavirus/dados-abertos/
SIVEP-Gripe · 2020
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Anonymization through data synthesis using generative adversarial networks (ads-gan)
Yoon, J., Drumright, L. N., and Van Der Schaar, M · 2020
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