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Softmax loss is arguably one of the most popular losses to train CNN models for image classification.
Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, and Y. Bengio · 1998
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The mnist database of handwritten digits
Y. LeCun, C. Cortes, and C. Burges · 1998
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Measuring statistical dependence with hilbert-schmidt norms
A. Gretton, O. Bousquet, and A. Smola · 2005
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On the relationship between spearman’s rho and kendall’s tau for pairs of continuous random variables
G. Fredricks and R. Nelsen · 2007
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Learning multiple layers of features from tiny images
A. Krizhevsky and G. Hinton · 2009
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Improving neural networks by preventing co-adaptation of feature detectors
G. Hinton, N. Srivastava, and A. Krizhevsky · 2012
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. Hinton · 2012
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Diversity regularized ensemble pruning
N. Li, Y. Yu, and Z. Zhou · 2012
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Maxout networks
I. Goodfellow, D. Warde-Farley, and M. Mirza · 2013
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Network in network
M. Lin, Q. Chen, and Y. Yan · 2013
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Deep learning using linear support vector machines
Y. Tang · 2013
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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
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and Z. Andrew · 2014
Cited alongside, same era.
Deep learning face representation by joint identification-verification
Y. Sun, Y. Chen, and X. Wang · 2014
Cited alongside, same era.
Learning visual similarity for product design with convolutional neural networks
S. Bell and K. Bala · 2015
Cited alongside, same era.
Deeply-supervised nets
Y. Lee, S. Xie, and P. Gallagher · 2015
Cited alongside, same era.
A discriminative feature learning approach for deep face recognition
Y. Wen, K. Zhang, and Z. Li · 2016
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Range loss for deep face recognition with long-tail
X. Zhang, Z. Fang, and Y. Wen · 2016
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Learning deep features for discriminative localization
B. Zhou, A. Khosla, and A. Lapedriza · 2016
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A downsampled variant of imagenet as an alternative to the cifar datasets
P. Chrabaszcz, L. Ilya, and H. Frank · 2017
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Exclusivity regularized machine
X. Guo, X. Wang, and H. Ling · 2017
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Soft-margin softmax for deep classification
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F. Schroff, D. Kalenichenko, and J. Philbin · 2015
Cited alongside, same era.
Adaptively unified semi-supervised dictionary learning with active points
X. Wang, X. Guo, and S. Li · 2015
Cited alongside, same era.
Deep residual learning for image recognition
K. He, X. Zhang, and S. Ren · 2016
Cited alongside, same era.
R-fcn: Object detection via region-based fully convolutional networks
Y. Li, K. He, and J. Sun · 2016
Cited alongside, same era.
Large-margin softmax loss for convolutional neural networks
W. Liu, Y. Wen, and Z. Yu · 2016
Cited alongside, same era.
From softmax to sparsemax: A sparse model of attention and multi-label classification
A. Martins and R. Astudillo · 2016
Cited alongside, same era.
X. Liang, X. Wang, Z. Lei, S. Liao, and Stan. Li · 2017
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Sphereface: Deep hypersphere embedding for face recognition
W. Liu, Y. Wen, Z. Yu, M. Li, and L. Song · 2017
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Learning deep features via congenerous cosine loss for person recognition
Y. Liu, H. Li, and X. Wang · 2017
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Normface: l 2 l_{2} hypersphere embedding for face verification.
F. Wang, X. Xiang, J. Chen, and A. Yuille · 2017
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Exclusivity-consistency regularized multi-view subspace clustering
X. Wang, X. Guo, Z. Lei, C. Zhang, and S. Li · 2017
Later among the works it cites.