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Trained generative models have shown remarkable performance as priors for inverse problems in imaging -- for example, Generative Adversarial Network priors permit recovery of test images from 5-10x fewer measurements than sparsity priors.
The approximation of one matrix by another of lower rank
Eckart, C. and Young, G · 1936
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Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A., Sheikh, H., and Simoncelli, E · 2003
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Image denoising by sparse 3-d transform-domain collaborative filtering
Dabov, K., Foi, A., Katkovnik, V., and Egiazarian, K · 2007
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Finding structure with randomness: probabilistic algorithms for constructing approximate matrix decompositions
Halko, N., Martinsson, P.-G., and Tropp, J. A · 2011
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Statistical compressed sensing of gaussian mixture models
Yu, G. and Sapiro, G · 2011
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Generative Adversarial Nets , pp. 2672–2680
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., 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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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 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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Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., and Chintala, 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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Density estimation using real nvp
Dinh, L., Sohl-Dickstein, J., and Bengio, S · 2017
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The reversible residual network: Backpropagation without storing activations
Gomez, A. N., Ren, M., Urtasun, R., and Grosse, R. B · 2017
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Efficient and accurate mri super-resolution using a generative adversarial network and 3d multi-level densely connected network
Chen, Y., Shi, F., Christodoulou, A. G., Xie, Y., Zhou, Z., and Li, D · 2018
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Global guarantees for enforcing deep generative priors by empirical risk
Finger-gan: Generating realistic fingerprint images using connectivity imposed gan
Minaee, S. and Abdolrashidi, A · 2018
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Medical image synthesis for data augmentation and anonymization using generative adversarial networks
Shin, H.-C., Tenenholtz, N. A., Rogers, J. K., Schwarz, C. G., Senjem, M. L., Gunter, J. L., Andriole, K. P., and Michalski, M · 2018
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Deep image prior
Ulyanov, D., Vedaldi, A., and Lempitsky, V. S · 2018
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Analyzing inverse problems with invertible neural networks
Ardizzone, L., Kruse, J., Rother, C., and Köthe, U · 2019
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Blind image deconvolution using deep generative priors
Asim, M., Shamshad, F., and Ahmed, A · 2019
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Hand, P. and Voroninski, V · 2018
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Phase Retrieval Under a Generative Prior , pp. 9136–9146
Hand, P., Leong, O., and Voroninski, V · 2018
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i-revnet: Deep invertible networks
Jacobsen, J.-H., Smeulders, A. W. M., and Oyallon, E · 2018
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Progressive growing of gans for improved quality, stability, and variation
Karras, T., Aila, T., Laine, S., and Lehtinen, J · 2018
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Glow: Generative flow with invertible 1x1 convolutions
Kingma, D. P. and Dhariwal, P · 2018
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Latent convolutional models
Athar, S., Burnaev, E., and Lempitsky, V · 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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A style-based generator architecture for generative adversarial networks
Karras, T., Laine, S., and Aila, T · 2019
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Invert to learn to invert
Putzky, P. and Welling, M · 2019
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Image-adaptive gan based reconstruction
Hussein, S. A., Tirer, T., and Giryes, R · 2020
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