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For most state-of-the-art architectures, Rectified Linear Unit (ReLU) becomes a standard component accompanied with each layer.
Learning multiple layers of features from tiny images
A. Krizhevsky and G. Hinton · 2009
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Rectified linear units improve restricted boltzmann machines
V. Nair and G. E. Hinton · 2010
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Deep sparse rectifier neural networks
X. Glorot, A. Bordes, and Y. Bengio · 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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M. Lin, Q. Chen, and S. Yan · 2013
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Rectifier nonlinearities improve neural network acoustic models
A. L. Maas, A. Y. Hannun, and A. Y. Ng · 2013
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Rich feature hierarchies for accurate object detection and semantic segmentation
R. Girshick, J. Donahue, T. Darrell, and J. Malik · 2014
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
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Visualizing and understanding convolutional networks
M. D. Zeiler and R. Fergus · 2014
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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
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
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Imagenet large scale visual recognition challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, et al · 2015
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Training very deep networks
R. K. Srivastava, K. Greff, and J. Schmidhuber · 2015
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Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2015
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Empirical evaluation of rectified activations in convolutional network
B. Xu, N. Wang, T. Chen, and M. Li · 2015
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J. L. Ba, J. R. Kiros, and G. E. Hinton · 2016
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Deep residual learning for image recognition
Wide residual networks
S. Zagoruyko and N. Komodakis · 2016
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The shattered gradients problem: If resnets are the answer, then what is the question?
D. Balduzzi, M. Frean, L. Leary, J. Lewis, K. W.-D. Ma, and B. McWilliams · 2017
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More is less: A more complicated network with less inference complexity
X. Dong, J. Huang, Y. Yang, and S. Yan · 2017
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Shake-shake regularization
X. Gastaldi · 2017
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Detecting and recognizing human-object intaractions
G. Gkioxari, R. Girshick, P. Dollár, and K. He · 2017
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Densely connected convolutional networks
G. Huang, Z. Liu, K. Q. Weinberger, and L. van der Maaten · 2017
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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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Rethinking the inception architecture for computer vision
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna · 2016
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Convolutional pose machines
S.-E. Wei, V. Ramakrishna, T. Kanade, and Y. Sheikh · 2016
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Inception-v4, inception-resnet and the impact of residual connections on learning
C. Szegedy, S. Ioffe, V. Vanhoucke, and A. A. Alemi · 2017
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Aggregated residual transformations for deep neural networks
S. Xie, R. Girshick, P. Dollár, Z. Tu, and K. He · 2017
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Diracnets: Training very deep neural networks without skip-connections
S. Zagoruyko and N. Komodakis · 2017
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Training better cnns requires to rethink relu
G. Zhao, Z. Zhang, J. Wang, and H. Guan · 2017
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