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Most modern convolutional neural networks (CNNs) used for object recognition are built using the same principles: Alternating convolution and max-pooling layers followed by a small number of fully connected layers.
Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
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Hierarchical neural networks for image interpretation
Behnke, Sven · 2003
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Imagenet: A large-scale hierarchical image database
Deng, Jia, Dong, Wei, Socher, Richard, jia Li, Li, Li, Kai, and Fei-fei, Li · 2009
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What is the best multi-stage architecture for object recognition?
Jarrett, Kevin, Kavukcuoglu, Koray, Ranzato, Marc’Aurelio, and LeCun, Yann · 2009
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Learning multiple layers of features from tiny images
Krizhevsky, A. and Hinton, G · 2009
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Improving neural networks by preventing co-adaptation of feature detectors
Hinton, Geoffrey E., Srivastava, Nitish, Krizhevsky, Alex, Sutskever, Ilya, and Salakhutdinov, Ruslan R · 2012
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Beyond spatial pyramids: Receptive field learning for pooled image features
Jia, Yangqing, Huang, Chang, and Darrell, Trevor · 2012
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Imagenet classification with deep convolutional neural networks
Krizhevsky, Alex, Sutskever, Ilya, and Hinton, Geoffrey E · 2012
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Maxout networks
Goodfellow, Ian J., Warde-Farley, David, Mirza, Mehdi, Courville, Aaron, and Bengio, Yoshua · 2013
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Improving deep neural networks with probabilistic maxout units
Springenberg, Jost Tobias and Riedmiller, Martin · 2013
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Discriminative transfer learning with tree-based priors
Srivastava, Nitish and Salakhutdinov, Ruslan · 2013
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Compete to compute
Srivastava, Rupesh K, Masci, Jonathan, Kazerounian, Sohrob, Gomez, Faustino, and Schmidhuber, Jürgen · 2013
Cited alongside, same era.
Regularization of neural networks using dropconnect
Wan, Li, Zeiler, Matthew D., Zhang, Sixin, LeCun, Yann, and Fergus, Rob · 2013
Cited alongside, same era.
Stochastic pooling for regularization of deep convolutional neural networks
Zeiler, Matthew D. and Fergus, Rob · 2013
Cited alongside, same era.
Signal recovery from pooling representations
Estrach, Joan B., Szlam, Arthur, and Lecun, Yann · 2014
Network in network
Lin, Min, Chen, Qiang, and Yan, Shuicheng · 2014
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Very deep convolutional networks for large-scale image recognition
Simonyan, Karen and Zisserman, Andrew · 2014
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Deep inside convolutional networks: Visualising image classification models and saliency maps
Simonyan, Karen, Vedaldi, Andrea, and Zisserman, Andrew · 2014
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Dropout: A simple way to prevent neural networks from overfitting
Srivastava, Nitish, Hinton, Geoffrey, Krizhevsky, Alex, Sutskever, Ilya, and Salakhutdinov, Ruslan · 2014
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Deep networks with internal selective attention through feedback connections
Stollenga, Marijn F, Masci, Jonathan, Gomez, Faustino, and Schmidhuber, Jürgen · 2014
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Cited alongside, same era.
Learned-norm pooling for deep feedforward and recurrent neural networks
Gülçehre, Çaglar, Cho, KyungHyun, Pascanu, Razvan, and Bengio, Yoshua · 2014
Cited alongside, same era.
Caffe: Convolutional architecture for fast feature embedding
Jia, Yangqing, Shelhamer, Evan, Donahue, Jeff, Karayev, Sergey, Long, Jonathan, Girshick, Ross, Guadarrama, Sergio, and Darrell, Trevor · 2014
Cited alongside, same era.
Deeply supervised nets
Lee, Chen-Yu, Xie, Saining, Gallagher, Patrick, Zhang, Zhengyou, and Tu, Zhuowen · 2014
Cited alongside, same era.
High-performance neural networks for visual object classification
Ciresan, Dan C., Meier, Ueli, Masci, Jonathan, Gambardella, Luca M., and Schmidhuber, Jürgen
Cited in the paper.
Szegedy, Christian, Liu, Wei, Jia, Yangqing, Sermanet, Pierre, Reed, Scott, Anguelov, Dragomir, Erhan, Dumitru, Vanhoucke, Vincent, and Rabinovich, Andrew · 2014
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Graham, Benjamin · 2015
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Visualizing and understanding convolutional networks
Zeiler, Matthew D. and Fergus, Rob · 2015
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