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In real-world face recognition applications, there is a tremendous amount of data with two images for each person.
J. Choe, S. Park, K. Kim, J. Hyun Park, D. Kim, and H. Shim, “Face generation for low-shot learning using generative adversarial networks,” in
1948
Earlier work this paper cites.
Y. Sun, Y. Chen, X. Wang, and X. Tang, “Deep learning face representation by joint identification-verification,” in
1996
Earlier work this paper cites.
Y. Bengio, R. Ducharme, P. Vincent, and C. Jauvin, “A neural probabilistic language model,”
2003
Earlier work this paper cites.
L. Feifei, R. Fergus, and P. Perona, “One-shot learning of object categories,”
2006
Earlier work this paper cites.
G. B. Huang, M. Mattar, T. Berg, and E. Learned-Miller, “Labeled faces in the wild: A database forstudying face recognition in unconstrained environments,” in
2008
Earlier work this paper cites.
D. J. Hsu, S. M. Kakade, J. Langford, and T. Zhang, “Multi-label prediction via compressed sensing,” in
2009
Earlier work this paper cites.
M. Gutmann and A. Hyvärinen, “Noise-contrastive estimation: A new estimation principle for unnormalized statistical models,” in
2010
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in
2012
Earlier work this paper cites.
2012
Earlier work this paper cites.
Y. Taigman, M. Yang, M. Ranzato, and L. Wolf, “Deepface: Closing the gap to human-level performance in face verification,” in
2013
Earlier work this paper cites.
Y. Sun, X. Wang, and X. Tang, “Deep learning face representation from predicting 10,000 classes,” in
2013
Earlier work this paper cites.
A. Choromanska, A. Agarwal, and J. Langford, “Extreme multi class classification,” in
2013
Earlier work this paper cites.
A. Mnih and K. Kavukcuoglu, “Learning word embeddings efficiently with noise-contrastive estimation,” in
2013
Earlier work this paper cites.
A. Vaswani, Y. Zhao, V. Fossum, and D. Chiang, “Decoding with large-scale neural language models improves translation,” in
2013
Earlier work this paper cites.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,”
2014
Earlier work this paper cites.
D. Yi, Z. Lei, S. Liao, and S. Z. Li, “Learning face representation from scratch,”
2014
Earlier work this paper cites.
Y. Taigman, M. Yang, M. Ranzato, and L. Wolf, “Web-scale training for face identification,”
2014
Earlier work this paper cites.
J. Weston, S. Chopra, and A. Bordes, “Memory networks,”
2014
Earlier work this paper cites.
J. Yang, B. Price, S. Cohen, and M. H. Yang, “Context driven scene parsing with attention to rare classes,” in
2014
Earlier work this paper cites.
Y. Prabhu and M. Varma, “Fastxml: A fast, accurate and stable tree-classifier for extreme multi-label learning,” in
2014
Earlier work this paper cites.
S. Liao, Z. Lei, D. Yi, and S. Z. Li, “A benchmark study of large-scale unconstrained face recognition,” in
2014
Earlier work this paper cites.
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in
2015
Earlier work this paper cites.
F. Schroff, D. Kalenichenko, and J. Philbin, “Facenet: A unified embedding for face recognition and clustering,” in
2015
Cited alongside, same era.
X. Wu, R. He, Z. Sun, and T. Tan, “A light cnn for deep face representation with noisy labels,”
2015
Cited alongside, same era.
G. Koch, R. Zemel, and R. Salakhutdinov, “Siamese neural networks for one-shot image recognition,” in
2015
Cited alongside, same era.
K. Bhatia, H. Jain, P. Kar, M. Varma, and P. Jain, “Sparse local embeddings for extreme multi-label classification,” in
2015
Cited alongside, same era.
2015
Cited alongside, same era.
C. Xu, D. Tao, and C. Xu, “Robust extreme multi-label learning,” in
2016
Later among the works it cites.
V. Balntas, E. Riba, D. Ponsa, and K. Mikolajczyk, “Learning local feature descriptors with triplets and shallow convolutional neural networks,” in
2016
Later among the works it cites.
E. Smirnov, A. Melnikov, S. Novoselov, E. Luckyanets, and G. Lavrentyeva, “Doppelganger mining for face representation learning,” in
2017
Later among the works it cites.
W. Chen, X. Chen, J. Zhang, and K. Huang, “Beyond triplet loss: A deep quadruplet network for person re-identification,”
2017
Later among the works it cites.
A. Nech and I. Kemelmacher-Shlizerman, “Level playing field for million scale face recognition,”
2017
Later among the works it cites.
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O. M. Parkhi, A. Vedaldi, A. Zisserman
2015
Cited alongside, same era.
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the inception architecture for computer vision,”
2016
Cited alongside, same era.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,”
2016
Cited alongside, same era.
C. Huang, C. C. Loy, and X. Tang, “Local similarity-aware deep feature embedding,” in
2016
Cited alongside, same era.
Y. Guo, L. Zhang, Y. Hu, X. He, and J. Gao, “Ms-celeb-1m: A dataset and benchmark for large-scale face recognition,” in
2016
Cited alongside, same era.
Y. Wen, K. Zhang, Z. Li, and Y. Qiao, “A discriminative feature learning approach for deep face recognition,” in
2016
Cited alongside, same era.
W. Liu, Y. Wen, Z. Yu, and M. Yang, “Large-margin softmax loss for convolutional neural networks.” in
2016
Cited alongside, same era.
2017
Later among the works it cites.
2017
Later among the works it cites.
F. Wang, X. Xiang, J. Cheng, and A. L. Yuille, “Normface:
2017
Later among the works it cites.
W. Liu, Y. Wen, Z. Yu, M. Li, B. Raj, and L. Song, “Sphereface: Deep hypersphere embedding for face recognition,” in
2017
Later among the works it cites.
W. Liu, Y.-M. Zhang, X. Li, Z. Yu, B. Dai, T. Zhao, and L. Song, “Deep hyperspherical learning,” in
2017
Later among the works it cites.
2017
Later among the works it cites.
C. Wang, X. Zhang, and X. Lan, “How to train triplet networks with 100k identities?”
2017
Later among the works it cites.
X. Zhang, Z. Fang, Y. Wen, Z. Li, and Y. Qiao, “Range loss for deep face recognition with long-tailed training data,” in
2017
Later among the works it cites.
Y. Guo and L. Zhang, “One-shot face recognition by promoting underrepresented classes,”
2017
Later among the works it cites.
R. Babbar and B. Schölkopf, “Dismec: distributed sparse machines for extreme multi-label classification,” in
2017
Later among the works it cites.
Y. Tagami, “Annexml: Approximate nearest neighbor search for extreme multi-label classification,” in
2017
Later among the works it cites.
2017
Later among the works it cites.
2017
Later among the works it cites.
C. Sun, A. Shrivastava, S. Singh, and A. Gupta, “Revisiting unreasonable effectiveness of data in deep learning era,” in
2017
Later among the works it cites.
F. Wang, W. Liu, H. Liu, and J. Cheng, “Additive margin softmax for face verification,”
2018
Closest in time.
Y. Zhao, Z. Jin, G. Qi, H. Lu, and X. Hua, “A principled approach to hard triplet generation via adversarial nets,” in
2018
Closest in time.
H. Wang, Y. Wang, Z. Zhou, X. Ji, and W. Liu, “Cosface: Large margin cosine loss for deep face recognition,” in
2018
Closest in time.