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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 / 1,916 papers shown
Paying More Attention to Attention: Improving the Performance of
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Sergey Zagoruyko
N. Komodakis
544
2,901
0
12 Dec 2016
Automatic Lymphocyte Detection in H&E Images with Deep Neural Networks
Automatic Lymphocyte Detection in H&E Images with Deep Neural Networks
Jianxu Chen
C. Srinivas
105
33
0
09 Dec 2016
Large-Margin Softmax Loss for Convolutional Neural Networks
Large-Margin Softmax Loss for Convolutional Neural Networks
Weiyang Liu
Yandong Wen
Zhiding Yu
Meng Yang
CVBM
328
1,541
0
07 Dec 2016
A Probabilistic Framework for Deep Learning
A Probabilistic Framework for Deep Learning
Ankit B. Patel
M. T. Nguyen
Richard G. Baraniuk
BDL
195
69
0
06 Dec 2016
Short-term traffic flow forecasting with spatial-temporal correlation in
  a hybrid deep learning framework
Short-term traffic flow forecasting with spatial-temporal correlation in a hybrid deep learning framework
Yuankai Wu
Huachun Tan
AI4TS
214
265
0
03 Dec 2016
Learning Deep Representations Using Convolutional Auto-encoders with
  Symmetric Skip Connections
Learning Deep Representations Using Convolutional Auto-encoders with Symmetric Skip Connections
Jianfeng Dong
Xiao-Jiao Mao
Chunhua Shen
Yubin Yang
SSL
156
6
0
28 Nov 2016
Texture Synthesis with Spatial Generative Adversarial Networks
Texture Synthesis with Spatial Generative Adversarial Networks
Nikolay Jetchev
Urs M. Bergmann
Roland Vollgraf
GAN
240
221
0
24 Nov 2016
Geometric deep learning: going beyond Euclidean data
Geometric deep learning: going beyond Euclidean data
M. Bronstein
Joan Bruna
Yann LeCun
Arthur Szlam
P. Vandergheynst
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EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer
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EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces
Vernon J. Lawhern
Amelia J. Solon
Nicholas R. Waytowich
Stephen M. Gordon
C. Hung
Brent Lance
OOD
738
3,645
0
23 Nov 2016
PVANet: Lightweight Deep Neural Networks for Real-time Object Detection
PVANet: Lightweight Deep Neural Networks for Real-time Object Detection
Sanghoon Hong
Byungseok Roh
Kye-Hyeon Kim
Yeongjae Cheon
Minje Park
ObjD
285
88
0
23 Nov 2016
Infinite Variational Autoencoder for Semi-Supervised Learning
Infinite Variational Autoencoder for Semi-Supervised Learning
Ehsan Abbasnejad
A. Dick
Anton Van Den Hengel
BDLDRL
151
86
0
23 Nov 2016
Deep Convolutional Neural Networks with Merge-and-Run Mappings
Deep Convolutional Neural Networks with Merge-and-Run Mappings
Liming Zhao
Jingdong Wang
Xi Li
Zhuowen Tu
Yueting Zhuang
MoMe
227
68
0
23 Nov 2016
Grad-CAM: Why did you say that?
Grad-CAM: Why did you say that?
Ramprasaath R. Selvaraju
Abhishek Das
Ramakrishna Vedantam
Michael Cogswell
Devi Parikh
Dhruv Batra
FAtt
349
566
0
22 Nov 2016
Improving training of deep neural networks via Singular Value Bounding
Improving training of deep neural networks via Singular Value Bounding
Kui Jia
AAML
262
94
0
18 Nov 2016
DelugeNets: Deep Networks with Efficient and Flexible Cross-layer
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DelugeNets: Deep Networks with Efficient and Flexible Cross-layer Information Inflows
Jason Kuen
Xiangfei Kong
G. Wang
Yap-Peng Tan
206
16
0
17 Nov 2016
Deep Feature Interpolation for Image Content Changes
Deep Feature Interpolation for Image Content Changes
P. Upchurch
Jacob V. Gardner
Geoff Pleiss
Robert Pless
Noah Snavely
Kavita Bala
Kilian Q. Weinberger
259
326
0
16 Nov 2016
On the Exploration of Convolutional Fusion Networks for Visual
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On the Exploration of Convolutional Fusion Networks for Visual Recognition
Y. Liu
Yanming Guo
M. Lew
173
25
0
16 Nov 2016
VisualBackProp: efficient visualization of CNNs
VisualBackProp: efficient visualization of CNNs
Mariusz Bojarski
A. Choromańska
K. Choromanski
Bernhard Firner
L. Jackel
Urs Muller
Karol Zieba
FAtt
180
76
0
16 Nov 2016
Will People Like Your Image? Learning the Aesthetic Space
Will People Like Your Image? Learning the Aesthetic Space
Katharina Schwarz
P. Wieschollek
Hendrik P. A. Lensch
128
51
0
16 Nov 2016
Identity Matters in Deep Learning
Identity Matters in Deep Learning
Moritz Hardt
Tengyu Ma
OOD
348
406
0
14 Nov 2016
A backward pass through a CNN using a generative model of its
  activations
A backward pass through a CNN using a generative model of its activations
Hua-Yan Wang
Anna Chen
Yi Liu
Dileep George
D. Phoenix
86
0
0
08 Nov 2016
Gradients of Counterfactuals
Gradients of Counterfactuals
Mukund Sundararajan
Ankur Taly
Qiqi Yan
FAtt
201
111
0
08 Nov 2016
Designing Neural Network Architectures using Reinforcement Learning
Designing Neural Network Architectures using Reinforcement Learning
Bowen Baker
O. Gupta
Nikhil Naik
Ramesh Raskar
347
1,530
0
07 Nov 2016
Regularizing CNNs with Locally Constrained Decorrelations
Regularizing CNNs with Locally Constrained Decorrelations
Pau Rodríguez López
Jordi Gonzalez
Guillem Cucurull
J. M. Gonfaus
F. X. Roca
224
141
0
07 Nov 2016
High-Resolution Semantic Labeling with Convolutional Neural Networks
High-Resolution Semantic Labeling with Convolutional Neural Networks
Emmanuel Maggiori
Y. Tarabalka
Guillaume Charpiat
Pierre Alliez
SSeg
119
186
0
07 Nov 2016
Entropy-SGD: Biasing Gradient Descent Into Wide Valleys
Entropy-SGD: Biasing Gradient Descent Into Wide Valleys
Pratik Chaudhari
A. Choromańska
Stefano Soatto
Yann LeCun
Carlo Baldassi
C. Borgs
J. Chayes
Levent Sagun
R. Zecchina
ODL
625
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LipNet: End-to-End Sentence-level Lipreading
LipNet: End-to-End Sentence-level Lipreading
Yannis Assael
Brendan Shillingford
Shimon Whiteson
Nando de Freitas
319
447
0
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Neural Architecture Search with Reinforcement Learning
Neural Architecture Search with Reinforcement Learning
Barret Zoph
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Information Dropout: Learning Optimal Representations Through Noisy
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Information Dropout: Learning Optimal Representations Through Noisy Computation
Alessandro Achille
Stefano Soatto
OODDRLSSL
328
431
0
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Learning Identity Mappings with Residual Gates
Learning Identity Mappings with Residual Gates
Pedro H. P. Savarese
Leonardo O. Mazza
Daniel R. Figueiredo
175
9
0
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Deep Convolutional Neural Network Design Patterns
Deep Convolutional Neural Network Design Patterns
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Nicholay Topin
AI4CEOOD
200
60
0
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Tensor Switching Networks
Tensor Switching Networks
Chuan-Yung Tsai
Andrew M. Saxe
David D. Cox
132
10
0
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Learning a Probabilistic Latent Space of Object Shapes via 3D
  Generative-Adversarial Modeling
Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling
Jiajun Wu
Chengkai Zhang
Tianfan Xue
Bill Freeman
J. Tenenbaum
GAN
719
2,078
0
24 Oct 2016
Optimization on Submanifolds of Convolution Kernels in CNNs
Optimization on Submanifolds of Convolution Kernels in CNNs
Mete Ozay
Takayuki Okatani
194
47
0
22 Oct 2016
Review of Action Recognition and Detection Methods
Review of Action Recognition and Detection Methods
Soo-Min Kang
Richard P. Wildes
183
59
0
21 Oct 2016
Learning Robust Video Synchronization without Annotations
Learning Robust Video Synchronization without Annotations
P. Wieschollek
Ido Freeman
Hendrik P. A. Lensch
226
7
0
19 Oct 2016
Deep Pyramidal Residual Networks
Deep Pyramidal Residual NetworksComputer Vision and Pattern Recognition (CVPR), 2016
Dongyoon Han
Jiwhan Kim
Junmo Kim
346
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10 Oct 2016
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based
  Localization
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based LocalizationInternational Journal of Computer Vision (IJCV), 2016
Ramprasaath R. Selvaraju
Michael Cogswell
Abhishek Das
Ramakrishna Vedantam
Devi Parikh
Dhruv Batra
FAtt
922
24,286
0
07 Oct 2016
Optimization of Convolutional Neural Network using Microcanonical
  Annealing Algorithm
Optimization of Convolutional Neural Network using Microcanonical Annealing AlgorithmInternational Conference on Advanced Computer Science and Information System (ICACSIS), 2016
Vina Ayumi
L. M. R. Rere
M. I. Fanany
A. M. Arymurthy
130
54
0
07 Oct 2016
Temporal Ensembling for Semi-Supervised Learning
Temporal Ensembling for Semi-Supervised LearningInternational Conference on Learning Representations (ICLR), 2016
S. Laine
Timo Aila
UQCV
545
2,764
0
07 Oct 2016
Dropout with Expectation-linear Regularization
Dropout with Expectation-linear Regularization
Xuezhe Ma
Yingkai Gao
Zhiting Hu
Yaoliang Yu
Yuntian Deng
Eduard H. Hovy
UQCV
203
52
0
26 Sep 2016
Optimistic and Pessimistic Neural Networks for Scene and Object
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Optimistic and Pessimistic Neural Networks for Scene and Object Recognition
René Grzeszick
Sebastian Sudholt
G. Fink
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188
4
0
26 Sep 2016
Production-Level Facial Performance Capture Using Deep Convolutional
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Production-Level Facial Performance Capture Using Deep Convolutional Neural Networks
S. Laine
Tero Karras
Timo Aila
Antti Herva
Forrest Iandola
Ronald Yu
Hao Li
J. Lehtinen
CVBM3DH
183
101
0
21 Sep 2016
Multi-Residual Networks: Improving the Speed and Accuracy of Residual
  Networks
Multi-Residual Networks: Improving the Speed and Accuracy of Residual Networks
M. Abdi
S. Nahavandi
145
45
0
19 Sep 2016
Associating Grasp Configurations with Hierarchical Features in
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Associating Grasp Configurations with Hierarchical Features in Convolutional Neural Networks
L. Ku
Erik Learned-Miller
R. Grupen
135
12
0
13 Sep 2016
A Greedy Algorithm to Cluster Specialists
A Greedy Algorithm to Cluster Specialists
Sébastien Arnold
74
0
0
13 Sep 2016
Fitted Learning: Models with Awareness of their Limits
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Navid Kardan
Kenneth O. Stanley
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230
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Towards Transparent AI Systems: Interpreting Visual Question Answering
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Towards Transparent AI Systems: Interpreting Visual Question Answering Models
Yash Goyal
Akrit Mohapatra
Devi Parikh
Dhruv Batra
110
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Densely Connected Convolutional Networks
Densely Connected Convolutional NetworksComputer Vision and Pattern Recognition (CVPR), 2016
Gao Huang
Zhuang Liu
Laurens van der Maaten
Kilian Q. Weinberger
PINN3DV
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41,141
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Lets keep it simple, Using simple architectures to outperform deeper and
  more complex architectures
Lets keep it simple, Using simple architectures to outperform deeper and more complex architectures
S. H. HasanPour
Mohammad Rouhani
Mohsen Fayyaz
Mohammad Sabokrou
481
126
0
22 Aug 2016
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