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The present paper studies so-called deep image prior (DIP) techniques in the context of ill-posed inverse problems.
Vieweg+Teubner Verlag, Wiesbaden (1989)
Louis, A.K.: Inverse und schlecht gestellte Probleme · 1989
Earlier work this paper cites.
Kluwer Academic Publishers Group, Dordrecht (1996)
Engl, H.W., Hanke, M., Neubauer, A.: Regularization of inverse problems, Mathematics and its Applications , vol. 375 · 1996
Earlier work this paper cites.
Vieweg, Wiesbaden (2003)
Rieder, A.: Keine Probleme mit inversen Problemen: eine Einfühhrung in ihre stabile Lösung · 2003
Earlier work this paper cites.
Communications on Pure and Applied Mathematics 57
Daubechies, I., Defrise, M., De Mol, C.: An iterative thresholding algorithm for linear inverse problems with a sparsity constraint · 2004
Earlier work this paper cites.
Multiscale Modeling & Simulation 4
Combettes, P., Wajs, V.: Signal recovery by proximal forward-backward splitting · 2005
Earlier work this paper cites.
Wavelets Xii, Pts 1 And 2 6701
Vonesch, C., Unser, M.: A fast iterative thresholding algorithm for wavelet-regularized deconvolution - art. no. 67010d · 2007
Earlier work this paper cites.
SIAM Journal on Imaging Sciences 2
Beck, A., Teboulle, M.: A fast iterative shrinkage-thresholding algorithm for linear inverse problems · 2009
Earlier work this paper cites.
In: ICML 2010 - Proceedings, 27th International Conference on Machine Learning, pp. 399–406 (2010)
Gregor, K., LeCun, Y.: Learning fast approximations of sparse coding · 2010
Earlier work this paper cites.
IEEE transactions on pattern analysis and machine intelligence 35
Bruna, J., Mallat, S.: Invariant scattering convolution networks · 2013
Earlier work this paper cites.
arXiv preprint arXiv:1312.6120 (2013)
Saxe, A.M., McClelland, J.L., Ganguli, S.: Exact solutions to the nonlinear dynamics of learning in deep linear neural networks · 2013
Earlier work this paper cites.
URL https://www.tensorflow.org/
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-scale machine learning on heterogeneous systems (2015) · 2015
Earlier work this paper cites.
IEEE transactions on pattern analysis and machine intelligence 37
Sprechmann, P., Bronstein, A.M., Sapiro, G.: Learning efficient sparse and low rank models · 2015
Earlier work this paper cites.
In: Advances in Neural Information Processing Systems, pp. 3981–3989 (2016)
Andrychowicz, M., Denil, M., Gomez, S., Hoffman, M.W., Pfau, D., Schaul, T., Shillingford, B., De Freitas, N.: Learning to learn by gradient descent by gradient descent · 2016
Cited alongside, same era.
arXiv preprint arXiv:1609.00285 (2016)
Moreau, T., Bruna, J.: Understanding trainable sparse coding via matrix factorization · 2016
Cited alongside, same era.
In: Advances in Neural Information Processing Systems, pp. 4340–4348 (2016)
Xin, B., Wang, Y., Gao, W., Wipf, D., Wang, B.: Maximal sparsity with deep networks? · 2016
Cited alongside, same era.
arXiv preprint arXiv:1611.03530 (2016)
Zhang, C., Bengio, S., Hardt, M., Recht, B., Vinyals, O.: Understanding deep learning requires rethinking generalization · 2016
Cited alongside, same era.
In: Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6-11 August 2017, pp. 537–546 (2017)
In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9446–9454 (2018)
Lempitsky, V., Vedaldi, A., Ulyanov, D.: Deep image prior · 2018
Closest in time.
IEEE Signal Processing Magazine 35
Papyan, V., Romano, Y., Sulam, J., Elad, M.: Theoretical foundations of deep learning via sparse representations: A multilayer sparse model and its connection to convolutional neural networks · 2018
Closest in time.
arXiv preprint arXiv:1806.06438 (2018)
Van Veen, D., Jalal, A., Price, E., Vishwanath, S., Dimakis, A.G.: Compressed sensing with deep image prior and learned regularization · 2018
Closest in time.
In: Submitted to International Conference on Learning Representations (2019)
Anonymous: On the spectral bias of neural networks · 2019
Closest in time.
Acta Numerica 28
Arridge, S., Maass, P., Öktem, O., Schönlieb, C.B.: Solving inverse problems using data-driven models · 2019
Closest in time.
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Bora, A., Jalal, A., Price, E., Dimakis, A.G.: Compressed sensing using generative models · 2017
Cited alongside, same era.
IEEE Transactions on Image Processing 26
Jin, K.H., McCann, M.T., Froustey, E., Unser, M.: Deep convolutional neural network for inverse problems in imaging · 2017
Cited alongside, same era.
In: IEEE International Conference on Computer Vision, pp. 1781–1790 (2017)
Meinhardt, T., Möller, M., Hazirbas, C., Cremers, D.: Learning proximal operators: Using denoising networks for regularizing inverse imaging problems · 2017
Cited alongside, same era.
IEEE transactions on medical imaging 37
Adler, J., Öktem, O.: Learned primal-dual reconstruction · 2018
Cited alongside, same era.
In: Advances in Neural Information Processing Systems, pp. 9061–9071 (2018)
Chen, X., Liu, J., Wang, Z., Yin, W.: Theoretical linear convergence of unfolded ista and its practical weights and thresholds · 2018
Cited alongside, same era.
Neural computation 8
Forster, D., Sheikh, A.S., Lücke, J.: Neural simpletrons: Learning in the limit of few labels with directed generative networks · 2018
Cited alongside, same era.
IEEE Transactions on Signal Processing 66
Giryes, R., Eldar, Y.C., Bronstein, A.M., Sapiro, G.: Tradeoffs between convergence speed and reconstruction accuracy in inverse problems · 2018
Cited alongside, same era.
IEEE transactions on medical imaging 37
Hauptmann, A., Lucka, F., Betcke, M., Huynh, N., Adler, J., Cox, B., Beard, P., Ourselin, S., Arridge, S.: Model-based learning for accelerated, limited-view 3-d photoacoustic tomography · 2018
Cited alongside, same era.
In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)
Cheng, Z., Gadelha, M., Maji, S., Sheldon, D.: A bayesian perspective on the deep image prior · 2019
Closest in time.
In: International Conference on Learning Representations (2019)
Liu, J., Chen, X., Wang, Z., Yin, W.: ALISTA: Analytic weights are as good as learned weights in LISTA · 2019
Closest in time.
Springer International Publishing, Cham (2019)
Maass, P.: Deep Learning for Trivial Inverse Problems, pp. 195–209 · 2019
Closest in time.
arXiv preprint arXiv:1903.10176 (2019)
Mataev, G., Elad, M., Milanfar, P.: Deepred: Deep image prior powered by red · 2019
Closest in time.
Springer Optimization and Its Applications. Springer International Publishing (2019)
Nesterov, Y.: Lectures on Convex Optimization · 2019
Closest in time.
IEEE transactions on pattern analysis and machine intelligence (2019)
Sulam, J., Aberdam, A., Beck, A., Elad, M.: On multi-layer basis pursuit, efficient algorithms and convolutional neural networks · 2019
Closest in time.