Fetching the paper…
Reading the bibliography…
Convolutional neural networks (CNNs) often perform well, but their stability is poorly understood.
A new method of graduation
Whittaker, E. T · 1923
Earlier work this paper cites.
Über variationsvermindernde lineare Transformationen
Schoenberg, I. J · 1930
Earlier work this paper cites.
Basic theory on normalization of pattern (in case of typical one-dimensional pattern)
Iijima, T · 1962
Earlier work this paper cites.
Solution of incorrectly formulated problems and the regularization method
Tikhonov, A. N · 1963
Earlier work this paper cites.
Robust regression: Asymptotics, conjectures and Monte Carlo
Huber, P. J · 1973
Earlier work this paper cites.
Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images
Geman, S. and Geman, D · 1984
Earlier work this paper cites.
Multilayer feedforward networks are universal approximators
Hornik, K., Stinchcombe, M., and White, H · 1989
Earlier work this paper cites.
Scale space and edge detection using anisotropic diffusion
Perona, P. and Malik, J · 1990
Earlier work this paper cites.
Nonlinear total variation based noise removal algorithms
Rudin, L. I., Osher, S., and Fatemi, E · 1992
Earlier work this paper cites.
Dynamics of neural networks with non-monotone activation function
De Felice, P., Marangi, C., Nardulli, G., Pasquariello, G., and Tedesco, L · 1993
Earlier work this paper cites.
Two deterministic half-quadratic regularization algorithms for computed imaging
Charbonnier, P., Blanc-Féraud, L., Aubert, G., and Barlaud, M · 1994
Earlier work this paper cites.
Ideal spatial adaptation by wavelet shrinkage
Donoho, D. L. and Johnstone, I. M · 1994
Earlier work this paper cites.
Optimal signalling in attractor neural networks
Meilijson, I. and Ruppin, E · 1994
Earlier work this paper cites.
Translation invariant denoising
Coifman, R. R. and Donoho, D · 1995
Earlier work this paper cites.
De-noising by soft thresholding
Donoho, D. L · 1995
Earlier work this paper cites.
A semidiscrete nonlinear scale-space theory and its relation to the Perona–Malik paradox
Weickert, J. and Benhamouda, B · 1997
Earlier work this paper cites.
Wavelet shrinkage denoising using the non-negative garrote
Gao, H · 1998
Earlier work this paper cites.
Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
Earlier work this paper cites.
Anisotropic Diffusion in Image Processing
Weickert, J · 1998
Earlier work this paper cites.
A Wavelet Tour of Signal Processing
Mallat, S · 1999
Earlier work this paper cites.
Calculus of Variations
Gelfand, I. M. and Fomin, S. V · 2000
Earlier work this paper cites.
Scale-space properties of nonstationary iterative regularization methods
Radmoser, E., Scherzer, O., and Weickert, J · 2000
Earlier work this paper cites.
Relations between regularization and diffusion filtering
Scherzer, O. and Weickert, J · 2000
Earlier work this paper cites.
Minimizing total variation flow
Andreu, F., Ballester, C., Caselles, V., and Mazón, J. M · 2001
Earlier work this paper cites.
Nonlinear anisotropic diffusion filters for wide range edge sharpening
Keeling, S. L. and Stollberger, R · 2002
Earlier work this paper cites.
Diffusion-inspired shrinkage functions and stability results for wavelet denoising
Mrázek, P., Weickert, J., and Steidl, G · 2005
Earlier work this paper cites.
Diffusion filters and wavelets: What can they learn from each other?
Weickert, J., Steidl, G., Mrázek, P., Welk, M., and Brox, T · 2006
Earlier work this paper cites.
Combined curvelet shrinkage and nonlinear anisotropic diffusion
Ma, J. and Plonka, G · 2007
Earlier work this paper cites.
A discriminative approach for wavelet denoising
Hel-Or, Y. and Shaked, D · 2008
Earlier work this paper cites.
Locally analytic schemes: A link between diffusion filtering and wavelet shrinkage
Welk, M., Steidl, G., and Weickert, J · 2008
Cited alongside, same era.
Rectified linear units improve restricted Boltzmann machines
Nair, V. and Hinton, G. E · 2010
Cited alongside, same era.
Invariant scattering convolution networks
Bruna, J. and Mallat, S · 2013
Cited alongside, same era.
A bilevel optimization approach for parameter learning in variational models
Kunisch, K. and Pock, T · 2013
Cited alongside, same era.
Shrinkage fields for effective image restoration
Schmidt, U. and Roth, S · 2014
Cited alongside, same era.
On learning optimized reaction diffusion processes for effective image restoration
Chen, Y., Yu, W., and Pock, T · 2015
Cited alongside, same era.
Essentially no barriers in neural network energy landscape
Draxler, F., Veschgini, K., Salmhofer, M., and Hamprecht, F · 2018
Later among the works it cites.
Wavelet convolutional neural networks
Fujieda, S., Takayama, K., and Hachisuka, T · 2018
Later among the works it cites.
xUnit: Learning a spatial activation function for efficient image restoration
Kligvasser, I., Shaham, T. R., and Michaeli, T · 2018
Later among the works it cites.
Visualizing the loss landscape of neural nets
Li, H., Xu, Z., Taylor, G., Studer, C., and Goldstein, T · 2018
Later among the works it cites.
Deep residual learning and PDEs on manifolds
Li, Z. and Shi, Z · 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…
Deep learning
LeCun, Y., Bengio, Y., and Hinton, G · 2015
Cited alongside, same era.
Deep learning in neural networks: An overview
Schmidhuber, J · 2015
Cited alongside, same era.
Trainable nonlinear reaction diffusion: A flexible framework for fast and effective image restoration
Chen, Y. and Pock, T · 2016
Cited alongside, same era.
Deep Learning
Goodfellow, I., Bengio, Y., and Courville, A · 2016
Cited alongside, same era.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Cited alongside, same era.
Bilevel parameter learning for higher-order total variation regularisation models
De los Reyes, J. C., Schönlieb, C., and Valkonen, T · 2017
Cited alongside, same era.
Rolnick, D. and Tegmark, M · 2018
Later among the works it cites.
Deep image prior
Ulyanov, D., Vedaldi, A., and Lempitsky, V · 2018
Later among the works it cites.
Wavelet pooling for convolutional neural networks
Williams, T. and Li, R · 2018
Later among the works it cites.
Deep layers as stochastic solvers
Bibi, A., Ghanem, B., Koltun, V., and Ranftl, R · 2019
Later among the works it cites.
Approximation spaces of deep neural networks
Gribonval, R., Kutyniok, G., Nielsen, M., and Voigtlaender, F · 2019
Later among the works it cites.
PDE-net 2.0: Learning PDEs from data with a numeric-symbolic hybrid deep network
Long, Z., Lu, Y., and Dong, B · 2019
Later among the works it cites.
Algorithm unrolling: Interpretable, efficient deep learning for signal and image processing
Monga, V., Li, Y., and Eldar, Y. C · 2019
Later among the works it cites.
On multi-layer basis pursuit, efficient algorithms and convolutional neural networks
Sulam, J., Aberdam, A., Beck, A., and Elad, M · 2019
Later among the works it cites.
Deep limits of residual neural networks
Thorpe, M. and van Gennip, Y · 2019
Later among the works it cites.
A representer theorem for deep neural networks
Unser, M · 2019
Later among the works it cites.
Learning a generic adaptive wavelet shrinkage function for denoising
Alt, T. and Weickert, J · 2020
Closest in time.
Networks for nonlinear diffusion problems in imaging
Arridge, S. and Hauptmann, A · 2020
Closest in time.
Learning stable nonlinear cross-diffusion models for image restoration
Barbeiro, S. and Lobo, D · 2020
Closest in time.
Deep neural network structures solving variational inequalities
Combettes, P. L. and Pesquet, J · 2020
Closest in time.
Regularization by architecture: A deep prior approach for inverse problems
Dittmer, S., Kluth, T., Maass, P., and Baguer, D. O · 2020
Closest in time.
Parseval proximal neural networks
Hasannasab, M., Hertrich, J., Neumayer, S., Plonka, G., Setzer, S., and Steidl, G · 2020
Closest in time.
Total deep variation for linear inverse problems
Kobler, E., Effland, A., Kunisch, K., and Pock, T · 2020
Closest in time.
Deep adaptive wavelet network
Rodriguez, M. X. B., Gruson, A., Polanía, L. F., Fujieda, S., Ortiz, F. P., Takayama, K., and Hachisuka, T · 2020
Closest in time.
Adversarial noise attacks of deep learning architectures: Stability analysis via sparse-modeled signals
Romano, Y., Aberdam, A., Sulam, J., and Elad, M · 2020
Closest in time.
Residual networks as flows of diffeomorphisms
Rousseau, F., Drumetz, L., and Fablet, R · 2020
Closest in time.
Deep neural networks motivated by partial differential equations
Ruthotto, L. and Haber, E · 2020
Closest in time.
PDE-based group equivariant convolutional neural networks
Smets, B., Portegies, J., Bekkers, E., and Duits, R · 2020
Closest in time.
Forward stability of ResNet and its variants
Zhang, L. and Schaeffer, H · 2020
Closest in time.