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Invariant Scattering Convolution Networks
v1v2 (latest)

Invariant Scattering Convolution Networks

5 March 2012
Joan Bruna
S. Mallat
ArXiv (abs)PDFHTML

Papers citing "Invariant Scattering Convolution Networks"

50 / 490 papers shown
Title
Deep Haar Scattering Networks in Pattern Recognition: A promising
  approach
Deep Haar Scattering Networks in Pattern Recognition: A promising approach
Fernando Fernandes Neto
A. Solomon
Rodrigo de Losso
Claudio Garcia
Pedro Delano Cavalcanti
22
0
0
29 Nov 2018
Deep Learning in the Wavelet Domain
Deep Learning in the Wavelet Domain
Fergal Cotter
N. Kingsbury
39
18
0
14 Nov 2018
Sorting out Lipschitz function approximation
Sorting out Lipschitz function approximation
Cem Anil
James Lucas
Roger C. Grosse
94
325
0
13 Nov 2018
Deep Neural Network Concepts for Background Subtraction: A Systematic
  Review and Comparative Evaluation
Deep Neural Network Concepts for Background Subtraction: A Systematic Review and Comparative Evaluation
T. Bouwmans
S. Javed
M. Sultana
Soon Ki Jung
84
322
0
13 Nov 2018
Excessive Invariance Causes Adversarial Vulnerability
Excessive Invariance Causes Adversarial Vulnerability
J. Jacobsen
Jens Behrmann
R. Zemel
Matthias Bethge
AAML
118
167
0
01 Nov 2018
A Bayesian Perspective of Convolutional Neural Networks through a
  Deconvolutional Generative Model
A Bayesian Perspective of Convolutional Neural Networks through a Deconvolutional Generative Model
Yujia Wang
Nhat Ho
David J. Miller
Anima Anandkumar
Michael I. Jordan
Richard G. Baraniuk
BDLGAN
96
8
0
01 Nov 2018
Multi-scale Geometric Summaries for Similarity-based Sensor Fusion
Multi-scale Geometric Summaries for Similarity-based Sensor Fusion
Christopher J. Tralie
Paul Bendich
J. Harer
8
2
0
13 Oct 2018
Bayesian Deep Convolutional Networks with Many Channels are Gaussian
  Processes
Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes
Roman Novak
Lechao Xiao
Jaehoon Lee
Yasaman Bahri
Greg Yang
Jiri Hron
Daniel A. Abolafia
Jeffrey Pennington
Jascha Narain Sohl-Dickstein
UQCVBDL
121
310
0
11 Oct 2018
Geometric Scattering for Graph Data Analysis
Geometric Scattering for Graph Data Analysis
Feng Gao
Guy Wolf
M. Hirn
GNN
91
113
0
07 Oct 2018
Encoding Robust Representation for Graph Generation
Encoding Robust Representation for Graph Generation
Dongmian Zou
Gilad Lerman
GNN
29
0
0
28 Sep 2018
Compressing the Input for CNNs with the First-Order Scattering Transform
Compressing the Input for CNNs with the First-Order Scattering Transform
Edouard Oyallon
Eugene Belilovsky
Sergey Zagoruyko
Michal Valko
62
25
0
27 Sep 2018
Scattering Networks for Hybrid Representation Learning
Scattering Networks for Hybrid Representation Learning
Edouard Oyallon
Sergey Zagoruyko
Gabriel Huang
N. Komodakis
Simon Lacoste-Julien
Matthew Blaschko
Eugene Belilovsky
66
86
0
17 Sep 2018
ManifoldNet: A Deep Network Framework for Manifold-valued Data
ManifoldNet: A Deep Network Framework for Manifold-valued Data
Rudrasis Chakraborty
Jose J. Bouza
J. Manton
B. Vemuri
43
16
0
11 Sep 2018
Isometric Transformation Invariant Graph-based Deep Neural Network
Isometric Transformation Invariant Graph-based Deep Neural Network
Renata Khasanova
P. Frossard
39
3
0
21 Aug 2018
A Hybrid Differential Evolution Approach to Designing Deep Convolutional
  Neural Networks for Image Classification
A Hybrid Differential Evolution Approach to Designing Deep Convolutional Neural Networks for Image Classification
Bin Wang
Yizhou Sun
Bing Xue
Mengjie Zhang
54
55
0
20 Aug 2018
On Lipschitz Bounds of General Convolutional Neural Networks
On Lipschitz Bounds of General Convolutional Neural Networks
Dongmian Zou
R. Balan
Maneesh Kumar Singh
65
54
0
04 Aug 2018
Generalization Error in Deep Learning
Generalization Error in Deep Learning
Daniel Jakubovitz
Raja Giryes
M. Rodrigues
AI4CE
226
111
0
03 Aug 2018
HybridNet: Classification and Reconstruction Cooperation for
  Semi-Supervised Learning
HybridNet: Classification and Reconstruction Cooperation for Semi-Supervised Learning
Thomas Robert
Nicolas Thome
Matthieu Cord
114
39
0
30 Jul 2018
An Algorithm for Learning Shape and Appearance Models without
  Annotations
An Algorithm for Learning Shape and Appearance Models without Annotations
John Ashburner
Mikael Brudfors
Kevin Bronik
Yael Balbastre
MedImFedML
25
11
0
27 Jul 2018
Scaled Simplex Representation for Subspace Clustering
Scaled Simplex Representation for Subspace Clustering
Jun Xu
Mengyang Yu
Ling Shao
Wangmeng Zuo
Deyu Meng
Lei Zhang
David C. Zhang
35
1
0
26 Jul 2018
Joint Time-Frequency Scattering
Joint Time-Frequency Scattering
Joakim Andén
Vincent Lostanlen
S. Mallat
AI4TS
77
54
0
24 Jul 2018
Fractional Wavelet Scattering Network and Applications
Fractional Wavelet Scattering Network and Applications
Li Liu
Jiasong Wu
Dengwang Li
L. Senhadji
H. Shu
MedIm
34
32
0
30 Jun 2018
Diffusion Scattering Transforms on Graphs
Diffusion Scattering Transforms on Graphs
Fernando Gama
Alejandro Ribeiro
Joan Bruna
GNN
94
103
0
22 Jun 2018
Sparse, Collaborative, or Nonnegative Representation: Which Helps
  Pattern Classification?
Sparse, Collaborative, or Nonnegative Representation: Which Helps Pattern Classification?
Jun Xu
Wangpeng An
Lei Zhang
David C. Zhang
BDL
132
120
0
12 Jun 2018
Building Bayesian Neural Networks with Blocks: On Structure,
  Interpretability and Uncertainty
Building Bayesian Neural Networks with Blocks: On Structure, Interpretability and Uncertainty
Hao Zhou
Yunyang Xiong
Vikas Singh
UQCVBDL
85
4
0
10 Jun 2018
Correspondence of Deep Neural Networks and the Brain for Visual Textures
Correspondence of Deep Neural Networks and the Brain for Visual Textures
Md Nasir Uddin Laskar
L. S. Giraldo
O. Schwartz
49
20
0
07 Jun 2018
Gradient-based Filter Design for the Dual-tree Wavelet Transform
Gradient-based Filter Design for the Dual-tree Wavelet Transform
D. Recoskie
Richard Mann
13
4
0
04 Jun 2018
Eye in the Sky: Real-time Drone Surveillance System (DSS) for Violent
  Individuals Identification using ScatterNet Hybrid Deep Learning Network
Eye in the Sky: Real-time Drone Surveillance System (DSS) for Violent Individuals Identification using ScatterNet Hybrid Deep Learning Network
Amarjot Singh
Devendra Patil
SN Omkar
88
119
0
03 Jun 2018
Interpreting Deep Learning: The Machine Learning Rorschach Test?
Interpreting Deep Learning: The Machine Learning Rorschach Test?
Adam S. Charles
AAMLHAIAI4CE
95
9
0
01 Jun 2018
Adversarial Examples in Remote Sensing
Adversarial Examples in Remote Sensing
W. Czaja
Neil Fendley
M. Pekala
Christopher R. Ratto
I-J. Wang
AAML
49
68
0
28 May 2018
Adversarially Robust Training through Structured Gradient Regularization
Adversarially Robust Training through Structured Gradient Regularization
Kevin Roth
Aurelien Lucchi
Sebastian Nowozin
Thomas Hofmann
72
23
0
22 May 2018
Generative networks as inverse problems with Scattering transforms
Generative networks as inverse problems with Scattering transforms
Tomás Angles
S. Mallat
GAN
67
31
0
17 May 2018
Mad Max: Affine Spline Insights into Deep Learning
Mad Max: Affine Spline Insights into Deep Learning
Randall Balestriero
Richard Baraniuk
AI4CE
82
78
0
17 May 2018
Monotone Learning with Rectified Wire Networks
Monotone Learning with Rectified Wire Networks
V. Elser
Dan Schmidt
J. Yedidia
21
0
0
10 May 2018
SHADE: Information Based Regularization for Deep Learning
SHADE: Information Based Regularization for Deep Learning
Michael Blot
Thomas Robert
Nicolas Thome
Matthieu Cord
62
12
0
29 Apr 2018
Universal approximations of invariant maps by neural networks
Universal approximations of invariant maps by neural networks
Dmitry Yarotsky
126
214
0
26 Apr 2018
CubeNet: Equivariance to 3D Rotation and Translation
CubeNet: Equivariance to 3D Rotation and Translation
Daniel E. Worrall
Gabriel J. Brostow
3DPC
90
143
0
12 Apr 2018
Pooling is neither necessary nor sufficient for appropriate deformation
  stability in CNNs
Pooling is neither necessary nor sufficient for appropriate deformation stability in CNNs
Avraham Ruderman
Neil C. Rabinowitz
Ari S. Morcos
Daniel Zoran
89
41
0
12 Apr 2018
Feature selection in weakly coherent matrices
Feature selection in weakly coherent matrices
Stéphane Chrétien
Z. Ho
16
1
0
03 Apr 2018
What Do We Understand About Convolutional Networks?
What Do We Understand About Convolutional Networks?
Isma Hadji
Richard P. Wildes
FAtt
57
98
0
23 Mar 2018
HATS: Histograms of Averaged Time Surfaces for Robust Event-based Object
  Classification
HATS: Histograms of Averaged Time Surfaces for Robust Event-based Object Classification
A. Sironi
Manuele Brambilla
Nicolas Bourdis
Xavier Lagorce
R. Benosman
77
449
0
21 Mar 2018
Evolving Deep Convolutional Neural Networks by Variable-length Particle
  Swarm Optimization for Image Classification
Evolving Deep Convolutional Neural Networks by Variable-length Particle Swarm Optimization for Image Classification
Bin Wang
Yizhou Sun
Bing Xue
Mengjie Zhang
92
168
0
17 Mar 2018
R3Net: Random Weights, Rectifier Linear Units and Robustness for
  Artificial Neural Network
R3Net: Random Weights, Rectifier Linear Units and Robustness for Artificial Neural Network
Arun Venkitaraman
Alireza M. Javid
Saikat Chatterjee
OODAAML
27
4
0
12 Mar 2018
Deep Dictionary Learning: A PARametric NETwork Approach
Deep Dictionary Learning: A PARametric NETwork Approach
Shahin Mahdizadehaghdam
Ashkan Panahi
Hamid Krim
Liyi Dai
68
63
0
11 Mar 2018
MIMO Graph Filters for Convolutional Neural Networks
MIMO Graph Filters for Convolutional Neural Networks
Fernando Gama
A. Marques
Alejandro Ribeiro
G. Leus
GNN
57
9
0
06 Mar 2018
Frank-Wolfe Network: An Interpretable Deep Structure for Non-Sparse
  Coding
Frank-Wolfe Network: An Interpretable Deep Structure for Non-Sparse Coding
Dong Liu
Ke Sun
Zhangyang Wang
Runsheng Liu
Zhengjun Zha
79
12
0
28 Feb 2018
The Role of Information Complexity and Randomization in Representation
  Learning
The Role of Information Complexity and Randomization in Representation Learning
Matías Vera
Pablo Piantanida
L. Rey Vega
80
14
0
14 Feb 2018
DCFNet: Deep Neural Network with Decomposed Convolutional Filters
DCFNet: Deep Neural Network with Decomposed Convolutional Filters
Qiang Qiu
Xiuyuan Cheng
Robert Calderbank
Guillermo Sapiro
71
69
0
12 Feb 2018
From BoW to CNN: Two Decades of Texture Representation for Texture
  Classification
From BoW to CNN: Two Decades of Texture Representation for Texture Classification
Li Liu
Jie Chen
Paul Fieguth
Guoying Zhao
Rama Chellappa
M. Pietikäinen
3DV
108
335
0
31 Jan 2018
Hierarchical Spatial Transformer Network
Hierarchical Spatial Transformer Network
Chang Shu
Xi Chen
Qiwei Xie
Hua Han
35
5
0
29 Jan 2018
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