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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"

16 / 1,916 papers shown
Unsupervised and Semi-supervised Learning with Categorical Generative
  Adversarial Networks
Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks
Jost Tobias Springenberg
GAN
214
768
0
19 Nov 2015
Why M Heads are Better than One: Training a Diverse Ensemble of Deep
  Networks
Why M Heads are Better than One: Training a Diverse Ensemble of Deep Networks
Stefan Lee
Senthil Purushwalkam
Michael Cogswell
David J. Crandall
Dhruv Batra
FedMLUQCV
188
332
0
19 Nov 2015
Classifying and Segmenting Microscopy Images Using Convolutional
  Multiple Instance Learning
Classifying and Segmenting Microscopy Images Using Convolutional Multiple Instance Learning
Oren Z. Kraus
Lei Jimmy Ba
B. Frey
313
407
0
17 Nov 2015
Do Deep Neural Networks Learn Facial Action Units When Doing Expression
  Recognition?
Do Deep Neural Networks Learn Facial Action Units When Doing Expression Recognition?
Pooya Khorrami
T. Paine
Thomas S. Huang
CVBM
248
275
0
10 Oct 2015
Deep Convolutional Neural Networks for Smile Recognition
Deep Convolutional Neural Networks for Smile Recognition
P. Glauner
CVBM
122
38
0
26 Aug 2015
What is Holding Back Convnets for Detection?
What is Holding Back Convnets for Detection?German Conference on Pattern Recognition (DAGM), 2015
Bojan Pepik
Rodrigo Benenson
Tobias Ritschel
Bernt Schiele
ObjD
224
65
0
12 Aug 2015
Training Very Deep Networks
Training Very Deep NetworksNeural Information Processing Systems (NeurIPS), 2015
R. Srivastava
Klaus Greff
Jürgen Schmidhuber
479
1,727
0
22 Jul 2015
Semi-Supervised Learning with Ladder Networks
Semi-Supervised Learning with Ladder NetworksNeural Information Processing Systems (NeurIPS), 2015
Antti Rasmus
Harri Valpola
Mikko Honkala
Mathias Berglund
T. Raiko
SSL
346
1,410
0
09 Jul 2015
Describing Multimedia Content using Attention-based Encoder--Decoder
  Networks
Describing Multimedia Content using Attention-based Encoder--Decoder Networks
Dong Wang
Aaron Courville
Yoshua Bengio
202
433
0
04 Jul 2015
Exploring the influence of scale on artist attribution
Exploring the influence of scale on artist attribution
Nanne van Noord
E. Postma
102
0
0
19 Jun 2015
Deep SimNets
Deep SimNetsComputer Vision and Pattern Recognition (CVPR), 2015
Nadav Cohen
Or Sharir
Amnon Shashua
194
46
0
09 Jun 2015
Inverting Visual Representations with Convolutional Networks
Inverting Visual Representations with Convolutional NetworksComputer Vision and Pattern Recognition (CVPR), 2015
Alexey Dosovitskiy
Thomas Brox
SSLFAtt
378
711
0
09 Jun 2015
Stacked What-Where Auto-encoders
Stacked What-Where Auto-encoders
Jiaqi Zhao
Michaël Mathieu
Ross Goroshin
Yann LeCun
DiffMBDL
640
263
0
08 Jun 2015
ReNet: A Recurrent Neural Network Based Alternative to Convolutional
  Networks
ReNet: A Recurrent Neural Network Based Alternative to Convolutional Networks
Francesco Visin
Kyle Kastner
Dong Wang
Matteo Matteucci
Aaron Courville
Yoshua Bengio
SSeg
194
277
0
03 May 2015
Scalable Bayesian Optimization Using Deep Neural Networks
Scalable Bayesian Optimization Using Deep Neural Networks
Jasper Snoek
Oren Rippel
Kevin Swersky
Ryan Kiros
N. Satish
N. Sundaram
Md. Mostofa Ali Patwary
P. Prabhat
Ryan P. Adams
BDLUQCV
278
1,101
0
19 Feb 2015
Batch Normalization: Accelerating Deep Network Training by Reducing
  Internal Covariate Shift
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Sergey Ioffe
Christian Szegedy
OOD
1.5K
45,717
0
11 Feb 2015
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