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Pre-trained diffusion models have been successfully used as priors in a variety of linear inverse problems, where the goal is to reconstruct a signal from noisy linear measurements.
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Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information
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Partially collapsed Gibbs samplers: Theory and methods
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Fast image deconvolution using hyper-Laplacian priors
Krishnan, D. and Fergus, R · 2009
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Speech dereverberation based on variance-normalized delayed linear prediction
Nakatani, T., Yoshioka, T., Kinoshita, K., Miyoshi, M., and Juang, B.-H · 2010
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Blind deconvolution of sparse pulse sequences under a minimum distance constraint: A partially collapsed Gibbs sampler method
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Xu, L., Zheng, S., and Jia, J · 2013
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An improved non-intrusive intelligibility metric for noisy and reverberant speech
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The ace challenge — corpus description and performance evaluation
Eaton, J., Gaubitch, N. D., Moore, A. H., and Naylor, P. A · 2015
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U-net: Convolutional networks for biomedical image segmentation
Ronneberger, O., Fischer, P., and Brox, T · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., and Ganguli, S · 2015
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Learning representations for automatic colorization
Larsson, G., Maire, M., and Shakhnarovich, G · 2016
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Blind image deblurring using dark channel prior
Pan, J., Sun, D., Pfister, H., and Yang, M.-H · 2016
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Bounds on the Jensen gap, and implications for mean-concentrated distributions
Gao, X., Sitharam, M., and Roitberg, A. E · 2017
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CNN architectures for large-scale audio classification
Hershey, S., Chaudhuri, S., Ellis, D. P. W., Gemmeke, J. F., Jansen, A., Moore, R. C., Plakal, M., Platt, D., Saurous, R. A., Seybold, B., Slaney, M., Weiss, R. J., and Wilson, K · 2017
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GANs trained by a two time-scale update rule converge to a local Nash equilibrium
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Learning to push the limits of efficient FFT-based image deconvolution
Kruse, J., Rother, C., and Schmidt, U · 2017
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Deblurring images via dark channel prior
Pan, J., Sun, D., Pfister, H., and Yang, M.-H · 2017
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One network to solve them all–solving linear inverse problems using deep projection models
Rick Chang, J., Li, C.-L., Poczos, B., Vijaya Kumar, B., and Sankaranarayanan, A. C · 2017
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Semantic image inpainting with deep generative models
Solving linear inverse problems using the prior implicit in a denoiser
Kadkhodaie, Z. and Simoncelli, E. P · 2020
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Neural blind deconvolution using deep priors
Ren, D., Zhang, K., Wang, Q., Hu, Q., and Zuo, W · 2020
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Improved techniques for training score-based generative models
Song, Y. and Ermon, S · 2020
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ILVR: Conditioning method for denoising diffusion probabilistic models
Choi, J., Kim, S., Jeong, Y., Gwon, Y., and Yoon, S · 2021
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Diffusion models beat GANs on image synthesis
Dhariwal, P. and Nichol, A · 2021
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Interspeech 2021 deep noise suppression challenge
K. A. Reddy, C., Dubey, H., Koishida, K., Asokan Nair, A., Gopal, V., Cutler, R., Braun, S., Gamper, H., Aichner, R., and Srinivasan, S · 2021
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An unsupervised approach to solving inverse problems using generative adversarial networks
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Mixed precision training
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Reverb conversion of mixed vocal tracks using an end-to-end convolutional deep neural network
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Improved denoising diffusion probabilistic models
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NHSS: A speech and singing parallel database
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Solving inverse problems in medical imaging with score-based generative models
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Solving inverse problems with a flow-based noise model
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Music enhancement via image translation and vocoding
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Denoising diffusion restoration models
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Improving score-based diffusion models by enforcing the underlying score Fokker-Planck equation
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On aliased resizing and surprising subtleties in gan evaluation
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MAXIM: Multi-axis MLP for image processing
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Unsupervised vocal dereverberation with diffusion-based generative models
Saito, K., Murata, N., Uesaka, T., Lai, C.-H., Takida, Y., Fukui, T., and Mitsufuji, Y · 2023
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