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Striving for Simplicity: The All Convolutional Net
v1v2v3 (latest)

Striving for Simplicity: The All Convolutional Net

International Conference on Learning Representations (ICLR), 2014
21 December 2014
Jost Tobias Springenberg
Alexey Dosovitskiy
Thomas Brox
Martin Riedmiller
    FAtt
ArXiv (abs)PDFHTML

Papers citing "Striving for Simplicity: The All Convolutional Net"

50 / 1,917 papers shown
Super-Resolution for Overhead Imagery Using DenseNets and Adversarial
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Super-Resolution for Overhead Imagery Using DenseNets and Adversarial Learning
Marc Bosch
C. Gifford
Pedro A. Rodriguez
GAN
102
27
0
28 Nov 2017
Providing theoretical learning guarantees to Deep Learning Networks
Providing theoretical learning guarantees to Deep Learning Networks
R. Mello
M. D. Ferreira
M. Ponti
117
6
0
28 Nov 2017
Pulsar Candidate Identification with Artificial Intelligence Techniques
Pulsar Candidate Identification with Artificial Intelligence Techniques
Ping Guo
Fuqing Duan
Pei Wang
Yao Yao
Qian Yin
Xin Xin
147
14
0
27 Nov 2017
An Introduction to Deep Visual Explanation
An Introduction to Deep Visual Explanation
H. Babiker
Randy Goebel
FAttAAML
132
19
0
26 Nov 2017
Training Confidence-calibrated Classifiers for Detecting
  Out-of-Distribution Samples
Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples
Kimin Lee
Honglak Lee
Kibok Lee
Jinwoo Shin
OODD
509
924
0
26 Nov 2017
Learning Less-Overlapping Representations
Learning Less-Overlapping Representations
P. Xie
Hongbao Zhang
Eric Xing
158
3
0
25 Nov 2017
Gradually Updated Neural Networks for Large-Scale Image Recognition
Gradually Updated Neural Networks for Large-Scale Image Recognition
Siyuan Qiao
Zhishuai Zhang
Wei Shen
Bo Wang
Alan Yuille
216
16
0
25 Nov 2017
CondenseNet: An Efficient DenseNet using Learned Group Convolutions
CondenseNet: An Efficient DenseNet using Learned Group Convolutions
Gao Huang
Shichen Liu
Laurens van der Maaten
Kilian Q. Weinberger
294
846
0
25 Nov 2017
Visual Feature Attribution using Wasserstein GANs
Visual Feature Attribution using Wasserstein GANs
Christian F. Baumgartner
Lisa M. Koch
K. Tezcan
Jia Xi Ang
E. Konukoglu
GANMedIm
267
153
0
24 Nov 2017
Critical Learning Periods in Deep Neural Networks
Critical Learning Periods in Deep Neural Networks
Alessandro Achille
Matteo Rovere
Stefano Soatto
272
114
0
24 Nov 2017
Variational Encoding of Complex Dynamics
Variational Encoding of Complex Dynamics
Carlos X. Hernández
H. Wayment-Steele
Mohammad M. Sultan
B. Husic
Vijay S. Pande
AI4CE
206
149
0
23 Nov 2017
Context Augmentation for Convolutional Neural Networks
Context Augmentation for Convolutional Neural Networks
Aysegül Dündar
Ignacio Garcia Dorado
122
5
0
22 Nov 2017
ForestHash: Semantic Hashing With Shallow Random Forests and Tiny
  Convolutional Networks
ForestHash: Semantic Hashing With Shallow Random Forests and Tiny Convolutional Networks
Qiang Qiu
José Lezama
A. Bronstein
Guillermo Sapiro
193
8
0
22 Nov 2017
Excitation Backprop for RNNs
Excitation Backprop for RNNs
Sarah Adel Bargal
Andrea Zunino
Donghyun Kim
Jianming Zhang
Vittorio Murino
Stan Sclaroff
279
48
0
18 Nov 2017
Learning to Play Othello with Deep Neural Networks
Learning to Play Othello with Deep Neural Networks
Paweł Liskowski
Wojciech Ja'skowski
K. Krawiec
66
27
0
17 Nov 2017
Using KL-divergence to focus Deep Visual Explanation
Using KL-divergence to focus Deep Visual Explanation
H. Babiker
Randy Goebel
FAtt
153
12
0
17 Nov 2017
A Resizable Mini-batch Gradient Descent based on a Multi-Armed Bandit
A Resizable Mini-batch Gradient Descent based on a Multi-Armed Bandit
S. Cho
Sunghun Kang
Chang D. Yoo
189
1
0
17 Nov 2017
A Forward-Backward Approach for Visualizing Information Flow in Deep
  Networks
A Forward-Backward Approach for Visualizing Information Flow in Deep Networks
Aditya Balu
THANH VAN NGUYEN
Apurva Kokate
Chinmay Hegde
Soumik Sarkar
FAtt
84
10
0
16 Nov 2017
Towards better understanding of gradient-based attribution methods for
  Deep Neural Networks
Towards better understanding of gradient-based attribution methods for Deep Neural Networks
Marco Ancona
Enea Ceolini
Cengiz Öztireli
Markus Gross
FAtt
398
148
0
16 Nov 2017
DLPaper2Code: Auto-generation of Code from Deep Learning Research Papers
DLPaper2Code: Auto-generation of Code from Deep Learning Research Papers
Akshay Sethi
A. Sankaran
Naveen Panwar
Shreya Khare
Senthil Mani
3DV
106
40
0
09 Nov 2017
The (Un)reliability of saliency methods
The (Un)reliability of saliency methods
Pieter-Jan Kindermans
Sara Hooker
Julius Adebayo
Maximilian Alber
Kristof T. Schütt
Sven Dähne
D. Erhan
Been Kim
FAttXAI
319
747
0
02 Nov 2017
Automatic calcium scoring in low-dose chest CT using deep neural
  networks with dilated convolutions
Automatic calcium scoring in low-dose chest CT using deep neural networks with dilated convolutions
Nikolas Lessmann
Bram van Ginneken
M. Zreik
P. D. de Jong
B. D. de Vos
M. Viergever
Ivana Išgum
178
211
0
01 Nov 2017
Visualizing and Understanding Atari Agents
Visualizing and Understanding Atari Agents
S. Greydanus
Anurag Koul
Jonathan Dodge
Alan Fern
FAtt
439
381
0
31 Oct 2017
CrescendoNet: A Simple Deep Convolutional Neural Network with Ensemble
  Behavior
CrescendoNet: A Simple Deep Convolutional Neural Network with Ensemble Behavior
Xiang Zhang
Nishant Vishwamitra
Hongxin Hu
Feng Luo
139
2
0
30 Oct 2017
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
Aditya Chattopadhyay
Anirban Sarkar
Prantik Howlader
V. Balasubramanian
FAtt
444
2,828
0
30 Oct 2017
Stochastic gradient descent performs variational inference, converges to
  limit cycles for deep networks
Stochastic gradient descent performs variational inference, converges to limit cycles for deep networks
Pratik Chaudhari
Stefano Soatto
MLT
259
316
0
30 Oct 2017
Discovery Radiomics with CLEAR-DR: Interpretable Computer Aided
  Diagnosis of Diabetic Retinopathy
Discovery Radiomics with CLEAR-DR: Interpretable Computer Aided Diagnosis of Diabetic RetinopathyIEEE Access (IEEE Access), 2017
Devinder Kumar
Graham W. Taylor
Alexander Wong
MedIm
123
39
0
29 Oct 2017
Label Embedding Network: Learning Label Representation for Soft Training
  of Deep Networks
Label Embedding Network: Learning Label Representation for Soft Training of Deep Networks
Xu Sun
Bingzhen Wei
Xuancheng Ren
Shuming Ma
183
43
0
28 Oct 2017
Dual Skipping Networks
Dual Skipping Networks
Changmao Cheng
Yanwei Fu
Yu-Gang Jiang
Wei Liu
Wenlian Lu
Jianfeng Feng
Xiangyang Xue
174
1
0
28 Oct 2017
Convolutional Neural Networks Via Node-Varying Graph Filters
Convolutional Neural Networks Via Node-Varying Graph FiltersData Science Workshop (DS), 2017
Fernando Gama
G. Leus
A. Marques
Alejandro Ribeiro
GNN
497
19
0
27 Oct 2017
Knowledge Projection for Deep Neural Networks
Knowledge Projection for Deep Neural Networks
Zhi Zhang
G. Ning
Zhihai He
124
15
0
26 Oct 2017
mixup: Beyond Empirical Risk Minimization
mixup: Beyond Empirical Risk MinimizationInternational Conference on Learning Representations (ICLR), 2017
Hongyi Zhang
Moustapha Cissé
Yann N. Dauphin
David Lopez-Paz
NoLa
741
11,163
0
25 Oct 2017
One pixel attack for fooling deep neural networks
One pixel attack for fooling deep neural networksIEEE Transactions on Evolutionary Computation (IEEE TEVC), 2017
Jiawei Su
Danilo Vasconcellos Vargas
Kouichi Sakurai
AAML
682
2,504
0
24 Oct 2017
A Survey of Model Compression and Acceleration for Deep Neural Networks
A Survey of Model Compression and Acceleration for Deep Neural Networks
Yu Cheng
Duo Wang
Pan Zhou
Zhang Tao
889
1,192
0
23 Oct 2017
Real-time Convolutional Neural Networks for Emotion and Gender
  Classification
Real-time Convolutional Neural Networks for Emotion and Gender Classification
Octavio Arriaga
Matias Valdenegro-Toro
Paul G. Plöger
3DH
133
305
0
20 Oct 2017
Do Convolutional Neural Networks Learn Class Hierarchy?
Do Convolutional Neural Networks Learn Class Hierarchy?
B. Alsallakh
Amin Jourabloo
Mao Ye
Xiaoming Liu
Liu Ren
266
226
0
17 Oct 2017
A systematic study of the class imbalance problem in convolutional
  neural networks
A systematic study of the class imbalance problem in convolutional neural networks
Mateusz Buda
A. Maki
Maciej A. Mazurowski
616
2,706
0
15 Oct 2017
Auto Analysis of Customer Feedback using CNN and GRU Network
Auto Analysis of Customer Feedback using CNN and GRU Network
D. Gupta
Pabitra Lenka
Harsimran Bedi
Asif Ekbal
P. Bhattacharyya
59
2
0
12 Oct 2017
Age Group and Gender Estimation in the Wild with Deep RoR Architecture
Age Group and Gender Estimation in the Wild with Deep RoR Architecture
Ke Zhang
Ce Gao
Liru Guo
Miao Sun
Xingfang Yuan
T. Han
Zhenbing Zhao
Baogang Li
CVBM
108
117
0
09 Oct 2017
Energy-Based Spherical Sparse Coding
Energy-Based Spherical Sparse Coding
Bailey Kong
Charless C. Fowlkes
79
1
0
04 Oct 2017
Improving Efficiency in Convolutional Neural Network with Multilinear
  Filters
Improving Efficiency in Convolutional Neural Network with Multilinear Filters
D. Tran
Alexandros Iosifidis
Moncef Gabbouj
251
43
0
28 Sep 2017
B-CNN: Branch Convolutional Neural Network for Hierarchical
  Classification
B-CNN: Branch Convolutional Neural Network for Hierarchical Classification
Xinqi Zhu
Michael Bain
281
169
0
28 Sep 2017
Efficient Convolutional Neural Network For Audio Event Detection
Efficient Convolutional Neural Network For Audio Event Detection
Matthias Meyer
Lukas Cavigelli
Lothar Thiele
120
22
0
28 Sep 2017
Comparison of Batch Normalization and Weight Normalization Algorithms
  for the Large-scale Image Classification
Comparison of Batch Normalization and Weight Normalization Algorithms for the Large-scale Image Classification
Igor Gitman
Boris Ginsburg
215
67
0
24 Sep 2017
Attention-based Wav2Text with Feature Transfer Learning
Attention-based Wav2Text with Feature Transfer Learning
Andros Tjandra
S. Sakti
Satoshi Nakamura
79
20
0
22 Sep 2017
Verifying Properties of Binarized Deep Neural Networks
Verifying Properties of Binarized Deep Neural Networks
Nina Narodytska
S. Kasiviswanathan
L. Ryzhyk
Shmuel Sagiv
T. Walsh
AAML
201
227
0
19 Sep 2017
Coupled Ensembles of Neural Networks
Coupled Ensembles of Neural Networks
Anuvabh Dutt
D. Pellerin
Georges Quénot
OOD
138
36
0
18 Sep 2017
Object Recognition from very few Training Examples for Enhancing Bicycle
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Object Recognition from very few Training Examples for Enhancing Bicycle Maps
Christoph Reinders
H. Ackermann
M. Yang
Bodo Rosenhahn
ObjD
236
0
0
18 Sep 2017
Learning Functional Causal Models with Generative Neural Networks
Learning Functional Causal Models with Generative Neural Networks
Hugo Jair Escalante
Sergio Escalera
Xavier Baro
Isabelle M Guyon
Umut Güçlü
Marcel van Gerven
CMLBDL
393
113
0
15 Sep 2017
Combining Strategic Learning and Tactical Search in Real-Time Strategy
  Games
Combining Strategic Learning and Tactical Search in Real-Time Strategy Games
Nicolas A. Barriga
Marius Stanescu
M. Buro
111
41
0
11 Sep 2017
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