Fetching the paper…
Reading the bibliography…
In this paper, we introduce convolutional proximal neural networks (cPNNs), which are by construction averaged operators.
Mean value methods in iteration
W. R. Mann · 1953
Earlier work this paper cites.
Two observations about the method of successive approximations
M. A. Krasnoselskii · 1955
Earlier work this paper cites.
Proximité et dualité dans un espace Hilbertien
J.-J. Moreau · 1965
Earlier work this paper cites.
Spectral and computational properties of band symmetric Toeplitz matrices
D. Bini and M. Capovani · 1983
Earlier work this paper cites.
Computing the polar decomposition–with applications
N. J. Higham · 1986
Earlier work this paper cites.
On the Douglas-Rachford splitting method and the proximal point algorithm for maximal monotone operators
J. Eckstein and D. P. Bertsekas · 1992
Earlier work this paper cites.
Nonlinear total variation based noise removal algorithms
L. Rudin, S. Osher, and E. Fatemi · 1992
Earlier work this paper cites.
Preconditioners for ill–conditioned Toeplitz matrices
D. Potts and G. Steidl · 1999
Earlier work this paper cites.
A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics
D. Martin, C. Fowlkes, D. Tal, and J. Malik · 2001
Earlier work this paper cites.
Γ \Gamma -Convergence for Beginners
A. Braides · 2002
Earlier work this paper cites.
An iterative thresholding algorithm for linear inverse problems with a sparsity constraint
I. Daubechies, M. Defrise, and C. De Mol · 2004
Earlier work this paper cites.
Signal recovery by proximal forward-backward splitting
P. L. Combettes and V. R. Wajs · 2005
Earlier work this paper cites.
Learning algorithms utilizing quasi-geodesic flows on the Stiefel manifold
Y. Nishimori and S. Akaho · 2005
Earlier work this paper cites.
Image denoising by sparse 3D transform-domain collaborative filtering
K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian · 2007
Earlier work this paper cites.
Scene-adapted plug-and-play algorithm with convergence guarantees
A. Teodoro, J. M. Bioucas-Dias, and M. Figueiredo · 2007
Earlier work this paper cites.
Optimization Algorithms on Matrix Manifolds
P.-A. Absil, R. Mahony, and R. Sepulchre · 2008
Earlier work this paper cites.
A new scaling for Newton’s iteration for the polar decomposition and its backward stability
R. Byers and H. Xu · 2008
Earlier work this paper cites.
Functions of Matrices: Theory and Computation
N. J. Higham · 2008
Earlier work this paper cites.
Message-passing algorithms for compressed sensing
D. L. Donoho, A. Maleki, and A. Montanari · 2009
Earlier work this paper cites.
Convex Analysis and Monotone Operator Theory in Hilbert Spaces
H. H. Bauschke and P. L. Combettes · 2011
Earlier work this paper cites.
Distributed optimization and statistical learning via the alternating direction method of multipliers
S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein · 2011
Cited alongside, same era.
Operator splittings, Bregman methods and frame shrinkage in image processing
S. Setzer · 2011
Cited alongside, same era.
Learning deep CNN denoiser prior for image restoration
K. Zhang, W. Zuo, S. Gu, and L. Zhang · 2011
Cited alongside, same era.
BM3D frames and variational image deblurring
A. Danielyan, V. Katkovnik, and K. Egiazarian · 2012
Cited alongside, same era.
Matrix Analysis
R. A. Horn and C. R. Johnson · 2013
Cited alongside, same era.
Plug-and-play priors for model based reconstruction
S. V. Venkatakrishnan, C. A. Bouman, and B. Wohlberg · 2013
Regularisation of neural networks by enforcing Lipschitz continuity
H. Gouk, E. Frank, B. Pfahringer, and M. Cree · 2018
Later among the works it cites.
CNN-based projected gradient descent for consistent CT image reconstruction
H. Gupta, K. H. Jin, H. Q. Nguyen, M. T. McCann, and M. Unser · 2018
Later among the works it cites.
Orthogonal weight normalization: Solution to optimization over multiple dependent Stiefel manifolds in deep neural networks
L. Huang, X. Liu, B. Lang, A. W. Yu, Y. Wang, and B. Li · 2018
Later among the works it cites.
Spectral normalization for generative adversarial networks
T. Miyato, T. Kataoka, M. Koyama, and Y. Yoshida · 2018
Later among the works it cites.
Quantitative convergence analysis of iterated expansive, set-valued mappings
D. Russell Luke, N. H. Thao, and M. K. Tam · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
A feasible method for optimization with orthogonality constraints
Z. Wen and W. Yin · 2013
Cited alongside, same era.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
Cited alongside, same era.
Functions of difference matrices are Toeplitz plus Hankel
G. Strang and S. MacNamara · 2014
Cited alongside, same era.
Compositions and convex combinations of averaged nonexpansive operators
P. L. Combettes and I. Yamada · 2015
Cited alongside, same era.
Estimation of the noise level function based on a nonparametric detection of homogeneous image regions
C. Sutour, C.-A. Deledalle, and J.-F. Aujol · 2015
Cited alongside, same era.
Plug-and-play ADMM for image restoration: Fixed-point convergence and applications
S. H. Chan, X. Wang, and O. A. Elgendy · 2016
Cited alongside, same era.
Lipschitz-margin training: Scalable certification of perturbation invariance for deep neural networks
Y. Tsuzuku, I. Sato, and M. Sugiyama · 2018
Later among the works it cites.
Proximal splitting algorithms: Relax them all!
L. Condat, D. Kitahara, A. Contreras, and A. Hirabayashi · 2019
Later among the works it cites.
Algorithm unrolling: Interpretable, efficient deep learning for signal and image processing
V. Monga, Y. Li, and Y. Eldar · 2019
Later among the works it cites.
The singular values of convolutional layers
H. Sedghi, V. Gupta, and P. M. Long · 2019
Later among the works it cites.
Energy dissipation with plug-and-play priors
H. Sommerhoff, A. Kolb, and M. Moeller · 2019
Later among the works it cites.
An online plug-and-play algorithm for regularized image reconstruction
Y. Sun, B. Wohlberg, and U. S. Kamilov · 2019
Later among the works it cites.
Understanding and mitigating exploding inverses in invertible neural networks
J. Behrmann, P. Vicol, K.-C. Wang, R. Grosse, and J.-H. Jacobsen · 2020
Closest in time.
Deep neural network structures solving variational inequalities
P. L. Combettes and J.-C. Pesquet · 2020
Closest in time.
Variational networks: an optimal control approach to early stopping variational methods for image restoration
A. Effland, E. Kobler, K. Kunisch, and T. Pock · 2020
Closest in time.
Stabilizing invertible neural networks using mixture models
P. Hagemann and S. Neumayer · 2020
Closest in time.
Parseval proximal neural networks
M. Hasannasab, J. Hertrich, S. Neumayer, G. Plonka, S. Setzer, and G. Steidl · 2020
Closest in time.
Inertial stochastic PALM and its application for learning Student- t t mixture models
J. Hertrich and G. Steidl · 2020
Closest in time.
Efficient Riemannian optimization on the Stiefel manifold via the Cayley transform
J. Li, F. Li, and S. Todorovic · 2020
Closest in time.
Building firmly nonexpansive convolutional neural networks
M. Terris, A. Repetti, J. Pesquet, and Y. Wiaux · 2020
Closest in time.