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In recent years, deep learning has achieved great success in many computer vision applications.
The influence of the sigmoid function parameters on the speed of backpropagation learning
J. Han and C. Moraga · 1995
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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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ImageNet Classification with Deep Convolutional Neural Networks
A. Krizhevsky, I. Sulskever, and G. E. Hinton · 2012
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Maxout networks
I. J. Goodfellow, D. Warde-Farley, M. Mirza, A. C. Courville, and Y. Bengio · 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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Compete to compute
R. K. Srivastava, J. Masci, S. Kazerounian, F. Gomez, and J. Schmidhuber · 2013
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cudnn: Efficient primitives for deep learning
S. Chetlur, C. Woolley, P. Vandermersch, J. Cohen, J. Tran, B. Catanzaro, and E. Shelhamer · 2014
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B. Graham · 2014
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Spatial pyramid pooling in deep convolutional networks for visual recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2014
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Caffe: Convolutional architecture for fast feature embedding
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell · 2014
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M. Kiefel, V. Jampani, and P. V. Gehler · 2014
Cited alongside, same era.
Learning deep features for scene recognition using places database
B. Zhou, A. Lapedriza, J. Xiao, A. Torralba, and A. Oliva · 2014
Cited alongside, same era.
Semantic image segmentation with deep convolutional nets and fully connected crfs
L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. Yuille · 2015
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Fast r-cnn
R. Girshick · 2015
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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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Rethinking the inception architecture for computer vision
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna · 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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Fast and accurate deep network learning by exponential linear units (elus)
D.-A. Clevert, T. Unterthiner, and S. Hochreiter · 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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S. Ioffe and C. Szegedy · 2015
Cited alongside, same era.
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, A. C. Berg, and L. Fei-Fei · 2015
Cited alongside, same era.
Very Deep Convolutional Networks for Large-Scale Image Recognition
K. Simonyan and A. Zisserman · 2015
Cited alongside, same era.
Striving for Simplicity: the All Convolutional Net
J. T. Springenberg, A. Dosovitskiy, T. Brox, and M. Riedmiller · 2015
Cited alongside, same era.
Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2015
Cited alongside, same era.
C.-Y. Lee, P. W. Gallagher, and Z. Tu · 2016
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Inception-v4, inception-resnet and the impact of residual connections on learning
C. Szegedy, S. Ioffe, and V. Vanhoucke · 2016
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Multi-scale context aggregation by dilated convolutions
F. Yu and V. Koltun · 2016
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S. Zagoruyko and N. Komodakis · 2016
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Places: An image database for deep scene understanding
B. Zhou, A. Khosla, A. Lapedriza, A. Torralba, and A. Oliva · 2016
Later among the works it cites.