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Rendition: Reclaiming what a black box takes away

Rendition: Reclaiming what a black box takes away

P. Milanfar
ArXiv (abs)PDFHTML

Papers citing "Rendition: Reclaiming what a black box takes away"

6 / 6 papers shown
Title
Lipschitz Properties for Deep Convolutional Networks
Lipschitz Properties for Deep Convolutional Networks
R. Balan
Maneesh Kumar Singh
Dongmian Zou
77
49
0
18 Jan 2017
The Little Engine that Could: Regularization by Denoising (RED)
The Little Engine that Could: Regularization by Denoising (RED)
Yaniv Romano
Michael Elad
P. Milanfar
DiffM
81
804
0
09 Nov 2016
Fast Multi-Layer Laplacian Enhancement
Fast Multi-Layer Laplacian Enhancement
Hossein Talebi Esfandarani
P. Milanfar
49
37
0
23 Jun 2016
Trainable Nonlinear Reaction Diffusion: A Flexible Framework for Fast and Effective Image Restoration
Trainable Nonlinear Reaction Diffusion: A Flexible Framework for Fast and Effective Image Restoration
Yunjin Chen
Thomas Pock
DiffM
54
1,190
0
12 Aug 2015
Boosting of Image Denoising Algorithms
Boosting of Image Denoising Algorithms
Yaniv Romano
Michael Elad
66
143
0
22 Feb 2015
Proximal Algorithms in Statistics and Machine Learning
Proximal Algorithms in Statistics and Machine Learning
Nicholas G. Polson
James G. Scott
Brandon T. Willard
299
149
0
11 Feb 2015

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