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We develop an iterative (greedy) deep learning (DL) algorithm which is able to transform an arbitrary probability distribution function (PDF) into the target PDF.
Some theorems on distribution functions
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Piecewise rational quadratic interpolation to monotonic data
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Exploratory projection pursuit
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Gaussianization
Chen, S. S. and Gopinath, R. A · 2000
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A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics
Martin, D., Fowlkes, C., Tal, D., and Malik, J · 2001
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Distributional sliced-wasserstein and applications to generative modeling
Nguyen, K., Ho, N., Pham, T., and Bui, H · 2002
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Automated colour grading using colour distribution transfer
Pitié, F., Kokaram, A. C., and Dahyot, R · 2007
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Optimal transport: old and new , volume 338
Villani, C · 2008
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Learning multiple layers of features from tiny images
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Integral geometry and Radon transforms
Helgason, S · 2010
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Improving relational regularized autoencoders with spherical sliced fused gromov wasserstein
Nguyen, K., Nguyen, S., Ho, N., Pham, T., and Bui, H · 2010
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Iterative gaussianization: from ica to random rotations
Laparra, V., Camps-Valls, G., and Malo, J · 2011
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Notes on optimization on stiefel manifolds
Tagare, H. D · 2011
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Uci machine learning repository, 2013
Lichman, M. et al · 2013
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Nice: Non-linear independent components estimation
Dinh, L., Krueger, D., and Bengio, Y · 2014
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Generative adversarial nets
Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A. C., and Bengio, Y · 2014
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Rezende, D. J., Mohamed, S., and Wierstra, D · 2014
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How (not) to train your generative model: Scheduled sampling, likelihood, adversary?
Huszár, F · 2015
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The radon cumulative distribution transform and its application to image classification
Kolouri, S., Park, S. R., and Rohde, G. K · 2015
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Human-level concept learning through probabilistic program induction
Lake, B. M., Salakhutdinov, R., and Tenenbaum, J. B · 2015
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Deep learning face attributes in the wild
Liu, Z., Luo, P., Wang, X., and Tang, X · 2015
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Variational inference with normalizing flows
Rezende, D. J. and Mohamed, S · 2015
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Multivariate density estimation: theory, practice, and visualization
Scott, D. W · 2015
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Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., and Chintala, S · 2016
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A note on the evaluation of generative models
Theis, L., van den Oord, A., and Bethge, M · 2016
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Towards principled methods for training generative adversarial networks
Image transformer
Parmar, N., Vaswani, A., Uszkoreit, J., Kaiser, L., Shazeer, N., Ku, A., and Tran, D · 2018
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Wasserstein auto-encoders
Tolstikhin, I. O., Bousquet, O., Gelly, S., and Schölkopf, B · 2018
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Diagnosing and enhancing VAE models
Dai, B. and Wipf, D. P · 2019
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Max-sliced wasserstein distance and its use for gans
Deshpande, I., Hu, Y., Sun, R., Pyrros, A., Siddiqui, N., Koyejo, S., Zhao, Z., Forsyth, D. A., and Schwing, A. G · 2019
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Neural spline flows
Durkan, C., Bekasov, A., Murray, I., and Papamakarios, G · 2019
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FFJORD: free-form continuous dynamics for scalable reversible generative models
Grathwohl, W., Chen, R. T. Q., Bettencourt, J., Sutskever, I., and Duvenaud, D · 2019
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Arjovsky, M. and Bottou, L · 2017
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Arjovsky, M., Chintala, S., and Bottou, L · 2017
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Density estimation using real NVP
Dinh, L., Sohl-Dickstein, J., and Bengio, S · 2017
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Improved training of wasserstein gans
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A. C · 2017
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Masked autoregressive flow for density estimation
Papamakarios, G., Murray, I., and Pavlakou, T · 2017
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Pixelcnn++: Improving the pixelcnn with discretized logistic mixture likelihood and other modifications
Salimans, T., Karpathy, A., Chen, X., and Kingma, D. P · 2017
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Neural discrete representation learning
van den Oord, A., Vinyals, O., and Kavukcuoglu, K · 2017
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A style-based generator architecture for generative adversarial networks
Karras, T., Laine, S., and Aila, T · 2019
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Generalized sliced wasserstein distances
Kolouri, S., Nadjahi, K., Simsekli, U., Badeau, R., and Rohde, G. K · 2019
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Sliced-wasserstein flows: Nonparametric generative modeling via optimal transport and diffusions
Liutkus, A., Simsekli, U., Majewski, S., Durmus, A., and Stöter, F · 2019
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Large-scale optimal transport map estimation using projection pursuit
Meng, C., Ke, Y., Zhang, J., Zhang, M., Zhong, W., and Ma, P · 2019
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Do deep generative models know what they don’t know?
Nalisnick, E. T., Matsukawa, A., Teh, Y. W., Görür, D., and Lakshminarayanan, B · 2019
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Subspace robust wasserstein distances
Paty, F. and Cuturi, M · 2019
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Likelihood ratios for out-of-distribution detection
Ren, J., Liu, P. J., Fertig, E., Snoek, J., Poplin, R., DePristo, M. A., Dillon, J. V., and Lakshminarayanan, B · 2019
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Orthogonal estimation of wasserstein distances
Rowland, M., Hron, J., Tang, Y., Choromanski, K., Sarlós, T., and Weller, A · 2019
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Stabilizing generative adversarial network training: A survey
Wiatrak, M. and Albrecht, S. V · 2019
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Sliced wasserstein generative models
Wu, J., Huang, Z., Acharya, D., Li, W., Thoma, J., Paudel, D. P., and Gool, L. V · 2019
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Generative latent flow: A framework for non-adversarial image generation
Xiao, Z., Yan, Q., Chen, Y., and Amit, Y · 2019
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Böhm, V. and Seljak, U · 2020
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Gaussianization flows
Meng, C., Song, Y., Song, J., and Ermon, S · 2020
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Statistical and topological properties of sliced probability divergences
Nadjahi, K., Durmus, A., Chizat, L., Kolouri, S., Shahrampour, S., and Simsekli, U · 2020
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Likelihood regret: An out-of-distribution detection score for variational auto-encoder
Xiao, Z., Yan, Q., and Amit, Y · 2020
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