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Learning deeper convolutional neural networks becomes a tendency in recent years.
Efficient backprop
LeCun, Y., Bottou, L., Orr, G., Muller, K.: · 1998
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Information Theoretic Neural Computation
Kamimura, R.: · 2002
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Elements of Information Theory
Cover, T.M., Thomas, J.A.: · 2006
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Rectified linear units improve restricted boltzmann machines
Nair, V., Hinton, G.: · 2010
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Understanding the difficulty of training deep feedforward neural networks
Glorot, X., Bengio, Y.: · 2010
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ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., Hinton, G.E.: · 2012
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Divide the gradient by a running average of its recent magnitude
Tieleman, T., Hinton, G.: · 2012
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Rectified nonlinearities improve neural network acstic models
Maas, A.L., Hannun, A.Y., Ng, A.Y.: · 2013
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Goodfellow, I.J., Warde-Farley, D., Mirza, M., Courville, A., Bengio, Y.: · 2013
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On the importance of initialization and momentum in deep learning
Sutskever, I., Martens, J., Dahl, G.E., Hinton, G.E.: · 2013
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Rich feature hierarchies for accurate object detection and semantic segmentation
Girshick, R., Donahue, J., Darrell, T., Malik, J.: · 2014
Cited alongside, same era.
Deepface: Closing the gap to human-level performance in face verification
Taigman, Y., Yang, M., Ranzato, M., Wolf, L.: · 2014
Cited alongside, same era.
Large-scale video classification with convolutional neural networks
Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., Fei-Fei, L.: · 2014
Cited alongside, same era.
Learning deep features for scene recognition using places database
Zhou, B., Lapedriza, A., Xiao, J., Torralba, A., Oliva, A.: · 2014
Cited alongside, same era.
Sun database: Exploring a large collection of scene categories
Xiao, J., Ehinger, K., Hays, J., Torralba, A., Oliva, A.: · 2014
Cited alongside, same era.
Visualizing and understanding convolutional neural networks
Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
He, K., Zhang, X., Ren, S., Sun, J.: · 2015
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Places2: A large-scale database for scene understanding
Zhou, B., Khosla, A., Lapedriza, A., Torralba, A., Oliva, A.: · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S., Szegedy, C.: · 2015
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Deep learning and the information bottleneck principle
Tishby, N., Zaslavsky, N.: · 2015
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Imagenet large scale visual recognition challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: · 2015
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Deeply-supervised nets
Lee, C.Y., Xie, S., Gallagher, P., Zhang, Z., Tu, Z.: · 2015
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Zeiler, M.D., Fergus, R.: · 2014
Cited alongside, same era.
Spatial pyramid pooling in deep convolutional networks for visual recognition
He, K., Zhang, X., Ren, S., Sun, J.: · 2014
Cited alongside, same era.
Caffe: Convolutional architecture for fast feature embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: · 2014
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
Simonyan, K., Zisserman, A.: · 2015
Cited alongside, same era.
Highway networks
Srivastava, R.K., Greff, K., Schmidhuber, J.: · 2015
Cited alongside, same era.
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Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: · 2015
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Rethinking the inception architecture for computer vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: · 2015
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., Sun, J.: · 2016
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