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Many generative models synthesize data by transforming a standard Gaussian random variable using a deterministic neural network.
Extremal properties of half-spaces for spherically invariant measures
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Gradient-based learning applied to document recognition
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Estimation of non-normalized statistical models by score matching
Hyvärinen, A. (2005) · 2005
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Generalization properties of optimal transport gans with latent distribution learning
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Blindness of score-based methods to isolated components and mixing proportions
Wenliang, L. K. and Kanagawa, H. (2020) · 2008
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A weak convergence approach to the theory of large deviations
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A connection between score matching and denoising autoencoders
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Rectifier nonlinearities improve neural network acoustic models
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Generative adversarial nets
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Auto-Encoding Variational Bayes
Kingma, D. P. and Welling, M. (2014) · 2014
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Explaining and harnessing adversarial examples
Goodfellow, I. J., Shlens, J., and Szegedy, C. (2015) · 2015
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J. (2015) · 2015
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Deep learning face attributes in the wild
Liu, Z., Luo, P., Wang, X., and Tang, X. (2015) · 2015
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Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., and Chintala, S. (2015) · 2015
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Variational inference with normalizing flows
Rezende, D. and Mohamed, S. (2015) · 2015
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U-net: Convolutional networks for biomedical image segmentation
Ronneberger, O., Fischer, P., and Brox, T. (2015) · 2015
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Imagenet large scale visual recognition challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · 2015
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J. (2016) · 2016
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Improved variational inference with inverse autoregressive flow
Kingma, D. P., Salimans, T., Jozefowicz, R., Chen, X., Sutskever, I., and Welling, M. (2016) · 2016
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Towards principled methods for training generative adversarial networks
Arjovsky, M. and Bottou, L. (2017) · 2017
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Wasserstein generative adversarial networks
Arjovsky, M., Chintala, S., and Bottou, L. (2017) · 2017
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Variational inference: A review for statisticians
Blei, D. M., Kucukelbir, A., and McAuliffe, J. D. (2017) · 2017
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Improved training of wasserstein gans
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A. C. (2017) · 2017
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Deligan: Generative adversarial networks for diverse and limited data
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Densely connected convolutional networks
Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K. Q. (2017) · 2017
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Image-to-image translation with conditional adversarial networks
Isola, P., Zhu, J.-Y., Zhou, T., and Efros, A. A. (2017) · 2017
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On convergence and stability of gans
Kodali, N., Abernethy, J., Hays, J., and Kira, Z. (2017) · 2017
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Photo-realistic single image super-resolution using a generative adversarial network
Ledig, C., Theis, L., Huszár, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., et al. (2017) · 2017
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Lim, J. H. and Ye, J. C. (2017) · 2017
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Improving neural machine translation with conditional sequence generative adversarial nets
Yang, Z., Chen, W., Wang, F., and Xu, B. (2018) · 2018
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Efficient and accurate estimation of Lipschitz constants for deep neural networks
Fazlyab, M., Robey, A., Hassani, H., Morari, M., and Pappas, G. (2019) · 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) · 2019
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Disconet: Shapes learning on disconnected manifolds for 3d editing
Mehr, E., Jourdan, A., Thome, N., Cord, M., and Guitteny, V. (2019) · 2019
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Computational optimal transport: with applications to data science
Peyré, G. and Cuturi, M. (2019) · 2019
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Resurrecting the sigmoid in deep learning through dynamical isometry: theory and practice
Pennington, J., Schoenholz, S., and Ganguli, S. (2017) · 2017
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Hierarchical implicit models and likelihood-free variational inference
Tran, D., Ranganath, R., and Blei, D. (2017) · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I. (2017) · 2017
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Augmenting image classifiers using data augmentation generative adversarial networks
Antoniou, A., Storkey, A., and Edwards, H. (2018) · 2018
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Large scale gan training for high fidelity natural image synthesis
Brock, A., Donahue, J., and Simonyan, K. (2018) · 2018
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Learning wasserstein embeddings
Courty, N., Flamary, R., and Ducoffe, M. (2018) · 2018
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Sandfort, V., Yan, K., Pickhardt, P. J., and Summers, R. M. (2019) · 2019
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Generative modeling by estimating gradients of the data distribution
Song, Y. and Ermon, S. (2019) · 2019
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Self-attention generative adversarial networks
Zhang, H., Goodfellow, I., Metaxas, D., and Odena, A. (2019) · 2019
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Relaxing bijectivity constraints with continuously indexed normalising flows
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Denoising diffusion probabilistic models
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Learning disconnected manifolds: Avoiding the no gan’s land by latent rejection
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Analyzing and improving the image quality of stylegan
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On implicit regularization in β \beta -vaes
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Implicit normalizing flows
Lu, C., Chen, J., Li, C., Wang, Q., and Zhu, J. (2020) · 2020
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The intrinsic dimension of images and its impact on learning
Pope, P., Zhu, C., Abdelkader, A., Goldblum, M., and Goldstein, T. (2020) · 2020
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Score-based generative modeling through stochastic differential equations
Song, Y., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Ermon, S., and Poole, B. (2020) · 2020
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Learning disconnected manifolds: a no gan’s land
Tanielian, U., Issenhuth, T., Dohmatob, E., and Mary, J. (2020) · 2020
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Stochastic normalizing flows
Wu, H., Köhler, J., and Noé, F. (2020) · 2020
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Understanding and mitigating exploding inverses in invertible neural networks
Behrmann, J., Vicol, P., Wang, K.-C., Grosse, R., and Jacobsen, J.-H. (2021) · 2021
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Diffusion models beat gans on image synthesis
Dhariwal, P. and Nichol, A. (2021) · 2021
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Stabilizing invertible neural networks using mixture models
Hagemann, P. and Neumayer, S. (2021) · 2021
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Skilful precipitation nowcasting using deep generative models of radar
Ravuri, S., Lenc, K., Willson, M., Kangin, D., Lam, R., Mirowski, P., Fitzsimons, M., Athanassiadou, M., Kashem, S., Madge, S., et al. (2021) · 2021
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Optimal 1-wasserstein distance for wgans
Stéphanovitch, A., Tanielian, U., Cadre, B., Klutchnikoff, N., and Biau, G. (2022) · 2022
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