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This paper proposes to learn high-performance deep ConvNets with sparse neural connections, referred to as sparse ConvNets, for face recognition.
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Labeled Faces in the Wild: A database for studying face recognition in unconstrained environments
G. B. Huang, M. Ramesh, T. Berg, and E. Learned-Miller · 2007
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Tom-vs-Pete classifiers and identity-preserving alignment for face verification
T. Berg and P. Belhumeur · 2012
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Bayesian face revisited: A joint formulation
D. Chen, X. Cao, L. Wang, F. Wen, and J. Sun · 2012
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G. E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov · 2012
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Provable bounds for learning some deep representations
S. Arora, A. Bhaskara, R. Ge, and T. Ma · 2013
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A practical transfer learning algorithm for face verification
X. Cao, D. Wipf, F. Wen, and G. Duan · 2013
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Blessing of dimensionality: High-dimensional feature and its efficient compression for face verification
D. Chen, X. Cao, F. Wen, and J. Sun · 2013
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Fisher vector faces in the wild
K. Simonyan, O. M. Parkhi, A. Vedaldi, and A. Zisserman · 2013
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J. Ba and R. Caruana · 2014
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Unconstrained face recognition: Identifying a person of interest from a media collection
L. Best-Rowden, H. Han, C. Otto, B. Klare, and A. K. Jain · 2014
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Deep learning face representation from predicting 10,000 classes
Y. Sun, X. Wang, and X. Tang · 2014
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DeepFace: Closing the gap to human-level performance in face verification
Y. Taigman, M. Yang, M. Ranzato, and L. Wolf · 2014
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Weight uncertainty in neural networks
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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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Deeply learned face representations are sparse, selective, and robust
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