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Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output.
The cascade-correlation learning architecture
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Backpropagation applied to handwritten zip code recognition
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Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
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Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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Learning multiple layers of features from tiny images
A. Krizhevsky and G. Hinton · 2009
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Neural network learning without backpropagation
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Deep sparse rectifier neural networks
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Reading digits in natural images with unsupervised feature learning, 2011
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Convolutional neural networks applied to house numbers digit classification
P. Sermanet, S. Chintala, and Y. LeCun · 2012
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Maxout networks
I. Goodfellow, D. Warde-Farley, M. Mirza, A. Courville, and Y. Bengio · 2013
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Pedestrian detection with unsupervised multi-stage feature learning
P. Sermanet, K. Kavukcuoglu, S. Chintala, and Y. LeCun · 2013
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On the importance of initialization and momentum in deep learning
I. Sutskever, J. Martens, G. Dahl, and G. Hinton · 2013
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Network in network
M. Lin, Q. Chen, and S. Yan · 2014
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Striving for simplicity: The all convolutional net
J. T. Springenberg, A. Dosovitskiy, T. Brox, and M. Riedmiller · 2014
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Dropout: a simple way to prevent neural networks from overfitting
N. Srivastava, G. E. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov · 2014
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Deep manifold traversal: Changing labels with convolutional features
J. R. Gardner, M. J. Kusner, Y. Li, P. Upchurch, K. Q. Weinberger, and J. E. Hopcroft · 2015
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A neural algorithm of artistic style
L. Gatys, A. Ecker, and M. Bethge · 2015
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Hypercolumns for object segmentation and fine-grained localization
B. Hariharan, P. Arbeláez, R. Girshick, and J. Malik · 2015
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Adanet: Adaptive structural learning of artificial neural networks
C. Cortes, X. Gonzalvo, V. Kuznetsov, M. Mohri, and S. Yang · 2016
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Training and investigating residual nets, 2016
S. Gross and M. Wilber · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Identity mappings in deep residual networks
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Deep networks with stochastic depth
G. Huang, Y. Sun, Z. Liu, D. Sedra, and K. Q. Weinberger · 2016
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Fractalnet: Ultra-deep neural networks without residuals
G. Larsson, M. Maire, and G. Shakhnarovich · 2016
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
K. He, X. Zhang, S. Ren, and J. Sun · 2015
Cited alongside, same era.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
Cited alongside, same era.
Deeply-supervised nets
C.-Y. Lee, S. Xie, P. Gallagher, Z. Zhang, and Z. Tu · 2015
Cited alongside, same era.
Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
Cited alongside, same era.
Semi-supervised learning with ladder networks
A. Rasmus, M. Berglund, M. Honkala, H. Valpola, and T. Raiko · 2015
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Fitnets: Hints for thin deep nets
A. Romero, N. Ballas, S. E. Kahou, A. Chassang, C. Gatta, and Y. Bengio · 2015
Cited alongside, same era.
Training very deep networks
R. K. Srivastava, K. Greff, and J. Schmidhuber · 2015
Cited alongside, same era.
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Bridging the gaps between residual learning, recurrent neural networks and visual cortex
Q. Liao and T. Poggio · 2016
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Deconstructing the ladder network architecture
M. Pezeshki, L. Fan, P. Brakel, A. Courville, and Y. Bengio · 2016
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Rethinking the inception architecture for computer vision
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna · 2016
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Resnet in resnet: Generalizing residual architectures
S. Targ, D. Almeida, and K. Lyman · 2016
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J. Wang, Z. Wei, T. Zhang, and W. Zeng · 2016
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S. Zagoruyko and N. Komodakis · 2016
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Augmenting supervised neural networks with unsupervised objectives for large-scale image classification
Y. Zhang, K. Lee, and H. Lee · 2016
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Memory-efficient implementation of densenets
G. Pleiss, D. Chen, G. Huang, T. Li, L. van der Maaten, and K. Q. Weinberger · 2017
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