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We study image inverse problems with a normalizing flow prior.
Invertible generative models for inverse problems: mitigating representation error and dataset bias
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Stable signal recovery from incomplete and inaccurate measurements
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Image denoising with block-matching and 3d filtering
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Compressed sensing
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Phase retrieval via wirtinger flow: Theory and algorithms
Candes, E. J., Li, X., and Soltanolkotabi, M · 2007
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Sparse mri: The application of compressed sensing for rapid mr imaging
Lustig, M., Donoho, D., and Pauly, J. M · 2007
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Prior image constrained compressed sensing (piccs): a method to accurately reconstruct dynamic ct images from highly undersampled projection data sets
Chen, G.-H., Tang, J., and Leng, S · 2008
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Model-based compressive sensing
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Mcmc using hamiltonian dynamics
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Bayesian learning via stochastic gradient langevin dynamics
Welling, M. and Teh, Y. W · 2011
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Estimating or propagating gradients through stochastic neurons for conditional computation
Bengio, Y., Léonard, N., and Courville, A · 2013
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2013
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A family of nonparametric density estimation algorithms
Tabak, E. G. and Turner, C. V · 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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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
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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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Density estimation using real nvp
Dinh, L., Sohl-Dickstein, J., and Bengio, S · 2016
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Compressed sensing using generative models
Bora, A., Jalal, A., Price, E., and Dimakis, A. G · 2017
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A deep learning method to estimate independent source number
Hu, W., Liu, R., Lin, X., Li, Y., Zhou, X., and He, X · 2017
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Deep image prior
Ulyanov, D., Vedaldi, A., and Lempitsky, V · 2018
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Compressed sensing with deep image prior and learned regularization
Van Veen, D., Jalal, A., Soltanolkotabi, M., Price, E., Vishwanath, S., and Dimakis, A. G · 2018
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Supervised speech separation based on deep learning: An overview
Wang, D. and Chen, J · 2018
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” double-dip”: Unsupervised image decomposition via coupled deep-image-priors
Gandelsman, Y., Shocher, A., and Irani, M · 2019
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Deep decoder: Concise image representations from untrained non-convolutional networks
Heckel, R. and Hand, P · 2019
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Towards unsupervised single-channel blind source separation using adversarial pair unmix-and-remix
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Ardizzone, L., Kruse, J., Wirkert, S. J., Rahner, D., Pellegrini, E. W., Klessen, R. S., Maier-Hein, L., Rother, C., and Köthe, U · 2018
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Waic, but why? generative ensembles for robust anomaly detection
Choi, H., Jang, E., and Alemi, A. A · 2018
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Modeling sparse deviations for compressed sensing using generative models, 2018
Dhar, M., Grover, A., and Ermon, S · 2018
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Benchmarking neural network robustness to common corruptions and surface variations
Hendrycks, D. and Dietterich, T. G · 2018
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Task-aware compressed sensing with generative adversarial networks
Kabkab, M., Samangouei, P., and Chellappa, R · 2018
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Glow: Generative flow with invertible 1x1 convolutions
Kingma, D. P. and Dhariwal, P · 2018
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Using deep neural networks for inverse problems in imaging: beyond analytical methods
Lucas, A., Iliadis, M., Molina, R., and Katsaggelos, A. K · 2018
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Hoshen, Y · 2019
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Inverting deep generative models, one layer at a time
Lei, Q., Jalal, A., Dhillon, I. S., and Dimakis, A. G · 2019
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Detecting out-of-distribution inputs to deep generative models using a test for typicality
Nalisnick, E., Matsukawa, A., Teh, Y. W., and Lakshminarayanan, B · 2019
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Asymptotics of map inference in deep networks
Pandit, P., Sahraee, M., Rangan, S., and Fletcher, A. K · 2019
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Normalizing flows for probabilistic modeling and inference
Papamakarios, G., Nalisnick, E., Rezende, D. J., Mohamed, S., and Lakshminarayanan, B · 2019
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Gan-based projector for faster recovery with convergence guarantees in linear inverse problems
Raj, A., Li, Y., and Bresler, Y · 2019
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Generative modeling by estimating gradients of the data distribution
Song, Y. and Ermon, S · 2019
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Block coordinate regularization by denoising
Sun, Y., Liu, J., and Kamilov, U. S · 2019
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Flow contrastive estimation of energy-based models
Gao, R., Nijkamp, E., Kingma, D. P., Xu, Z., Dai, A. M., and Wu, Y. N · 2020
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Global guarantees for enforcing deep generative priors by empirical risk
Hand, P. and Voroninski, V · 2020
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Source separation with deep generative priors
Jayaram, V. and Thickstun, J · 2020
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Information-theoretic lower bounds for compressive sensing with generative models
Liu, Z. and Scarlett, J · 2020
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Pulse: Self-supervised photo upsampling via latent space exploration of generative models
Menon, S., Damian, A., Hu, S., Ravi, N., and Rudin, C · 2020
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