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Over the past few years, softmax and SGD have become a commonly used component and the default training strategy in CNN frameworks, respectively.
An equivalence between sigmoidal gain scaling and training with noisy (jittered) input data
R. Reed, R. J. Marks, and S. Oh · 1992
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Regularization using jittered training data
R. Reed, S. Oh, and R. J. Marks · 1992
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A recurrent network that performs a context-sensitive prediction task
M. Steijvers and P. Grunwald · 2000
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Labeled faces in the wild: A database forstudying face recognition in unconstrained environments
G. B. Huang, M. Mattar, T. Berg, and E. Learned-Miller · 2008
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What is the best multi-stage architecture for object recognition?
K. Jarrett, K. Kavukcuoglu, M. Ranzato, and Y. Lecun · 2009
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Deep sparse rectifier neural networks
X. Glorot, A. Bordes, and Y. Bengio · 2010
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Practical variational inference for neural networks
A. Graves · 2011
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Face recognition in unconstrained videos with matched background similarity
L. Wolf, T. Hassner, and I. Maoz · 2011
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Learning multiple layers of features from tiny images
A. Krizhevsky · 2012
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Estimating or propagating gradients through stochastic neurons
Y. Bengio · 2013
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Maxout networks
I. J. Goodfellow, D. Warde-Farley, M. Mirza, A. Courville, and Y. Bengio · 2013
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Network in network
M. Lin, Q. Chen, and S. Yan · 2013
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Multi-task learning in deep neural networks for improved phoneme recognition
M. L. Seltzer and J. Droppo · 2013
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Stochastic pooling for regularization of deep convolutional neural networks
M. D. Zeiler and R. Fergus · 2013
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Unconstrained face recognition: Identifying a person of interest from a media collection
L. Best-Rowden, H. Han, C. Otto, B. F. Klare, and A. K. Jain · 2014
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Fractional max-pooling
B. Graham · 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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Deep learning face representation by joint identification-verification
Y. Sun, X. Wang, and X. Tang · 2014
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Deeply learned face representations are sparse, selective, and robust
Y. Sun, X. Wang, and X. Tang · 2014
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Caffe: Convolutional architecture for fast feature embedding
V. Turchenko and A. Luczak · 2014
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Learning face representation from scratch
D. Yi, Z. Lei, S. Liao, and S. Z. Li · 2014
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Weight uncertainty in neural networks
C. Blundell, J. Cornebise, K. Kavukcuoglu, and D. Wierstra · 2015
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Rectified linear units improve restricted boltzmann machines
by V Nair and G. E. Hinton · 2015
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Deep face recognition
O. M. Parkhi, A. Vedaldi, and A. Zisserman · 2015
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Facenet: A unified embedding for face recognition and clustering
F. Schroff, D. Kalenichenko, and J. Philbin · 2015
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
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Highway networks
R. K. Srivastava, K. Greff, and J. Schmidhuber · 2015
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Sparsifying neural network connections for face recognition
Y. Sun, X. Wang, and X. Tang · 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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Robust face recognition via multimodal deep face representation for multimedia applications
C. Ding and D. Tao · 2015
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2015
Cited alongside, same era.
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.
Part-stacked cnn for fine-grained visual categorization
S. Huang, Z. Xu, D. Tao, and Y. Zhang · 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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Fine-grained recognition without part annotations
J. Krause, H. Jin, J. Yang, and F. F. Li · 2015
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A lightened cnn for deep face representation
X. Wu, R. He, and Z. Sun · 2015
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Noisy activation functions
C. Gulcehre, M. Moczulski, M. Denil, and Y. Bengio · 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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Densely connected convolutional networks
G. Huang, Z. Liu, and K. Weinberger · 2016
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Deep networks with stochastic depth
G. Huang, Y. Sun, Z. Liu, D. Sedra, and K. Weinberger · 2016
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Ti-pooling: transformation-invariant pooling for feature learning in convolutional neural networks
D. Laptev, N. Savinov, J. M. Buhmann, and M. Pollefeys · 2016
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Large-margin softmax loss for convolutional neural networks
W. Liu, Y. Wen, Z. Yu, and M. Yang · 2016
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Understanding and improving convolutional neural networks via concatenated rectified linear units
W. Shang, K. Sohn, D. Almeida, and H. Lee · 2016
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Mask-cnn: Localizing parts and selecting descriptors for fine-grained image recognition
X. S. Wei, C. W. Xie, and J. Wu · 2016
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Disturblabel: Regularizing cnn on the loss layer
L. Xie, J. Wang, Z. Wei, M. Wang, and Q. Tian · 2016
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N. Zhang and W. Deng · 2016
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