Fetching the paper…
Reading the bibliography…
Face representation learning using datasets with a massive number of identities requires appropriate training methods.
S. Chopra, R. Hadsell, and Y. LeCun, “Learning a similarity metric discriminatively, with application to face verification,” in CVPR
2005
Earlier work this paper cites.
G. B. Huang, M. Mattar, T. Berg, and E. Learned-Miller, “Labeled faces in the wild: A database for studying face recognition in unconstrained environments,” in Workshop on faces in’Real-Life’Images: detection, alignment, and recognition
2008
Earlier work this paper cites.
Z. Liu, W. Hong, H. Zhang, and J. Ma, “Face recognition with dense supervision,” Neurocomputing
2011
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 CVPR
2014
Earlier work this paper cites.
G. Hinton, Y. LeCun, and Y. Bengio, “Deep learning,” Nature
2015
Earlier work this paper cites.
F. Schroff, D. Kalenichenko, and J. Philbin, “FaceNet: A unified embedding for face recognition and clustering,” in CVPR
2015
Earlier work this paper cites.
O. M. Parkhi, A. Vedaldi, A. Zisserman, et al
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
Y. Guo, L. Zhang, Y. Hu, X. He, and J. Gao, “MS-Celeb-1M: A dataset and benchmark for large-scale face recognition,” in ECCV
2016
Earlier work this paper cites.
I. Kemelmacher-Shlizerman, S. M. Seitz, D. Miller, and E. Brossard, “The MegaFace benchmark: 1 million faces for recognition at scale,” in CVPR
2016
Earlier work this paper cites.
S. Sengupta, J.-C. Chen, C. Castillo, V. M. Patel, R. Chellappa, and D. W. Jacobs, “Frontal to profile face verification in the wild,” in WACV
2016
Earlier work this paper cites.
K. Zhang, Z. Zhang, Z. Li, and Y. Qiao, “Joint face detection and alignment using multitask cascaded convolutional networks,” IEEE Signal Processing Letters
2016
Earlier work this paper cites.
Y. Wen, K. Zhang, Z. Li, and Y. Qiao, “A discriminative feature learning approach for deep face recognition,” in ECCV
2016
Earlier work this paper cites.
Y. Wu, J. Li, Y. Kong, and Y. Fu, “Deep convolutional neural network with independent softmax for large scale face recognition,” in ACM Multimedia
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in CVPR
2016
Earlier work this paper cites.
S. Moschoglou, A. Papaioannou, C. Sagonas, J. Deng, I. Kotsia, and S. Zafeiriou, “AgeDB: the first manually collected, in-the-wild age database,” in CVPR Workshops
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
W. Liu, Y. Wen, Z. Yu, M. Li, B. Raj, and L. Song, “SphereFace: Deep hypersphere embedding for face recognition,” in CVPR
2017
Earlier work this paper cites.
A. Bansal, A. Nanduri, C. D. Castillo, R. Ranjan, and R. Chellappa, “UMDFaces: An annotated face dataset for training deep networks,” in IJCB
2017
Earlier work this paper cites.
E. Smirnov, A. Melnikov, S. Novoselov, E. Luckyanets, and G. Lavrentyeva, “Doppelganger mining for face representation learning,” in ICCV Workshops
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
F. Wang, X. Xiang, J. Cheng, and A. L. Yuille, “NormFace: l _ 2 l\_2 hypersphere embedding for face verification,” in ACM Multimedia
2017
Earlier work this paper cites.
K. Shim, M. Lee, I. Choi, Y. Boo, and W. Sung, “SVD-Softmax: Fast softmax approximation on large vocabulary neural networks,” in NeurIPS
2017
Earlier work this paper cites.
E. Grave, A. Joulin, M. Cissé, D. Grangier, and H. Jégou, “Efficient softmax approximation for gpus,” in ICML
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
J. Snell, K. Swersky, and R. Zemel, “Prototypical networks for few-shot learning,” in NeurIPS
2017
Earlier work this paper cites.
Z. Wang, K. He, Y. Fu, R. Feng, Y.-G. Jiang, and X. Xue, “Multi-task deep neural network for joint face recognition and facial attribute prediction,” in ICMR
2017
Earlier work this paper cites.
J. Deng, Y. Zhou, and S. Zafeiriou, “Marginal loss for deep face recognition,” in CVPR Workshops
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
H. Wang, Y. Wang, Z. Zhou, X. Ji, Z. Li, D. Gong, J. Zhou, and W. Liu, “CosFace: Large margin cosine loss for deep face recognition,” in CVPR
2018
Earlier work this paper cites.
T. Zheng and W. Deng, “Cross-Pose LFW: A database for studying cross-pose face recognition in unconstrained environments,” Beijing University of Posts and Telecommunications, Tech. Rep. 18-01
2018
Earlier work this paper cites.
B. Maze, J. Adams, J. A. Duncan, N. Kalka, T. Miller, C. Otto, A. K. Jain, W. T. Niggel, J. Anderson, J. Cheney, et al
2018
Earlier work this paper cites.
Q. Cao, L. Shen, W. Xie, O. M. Parkhi, and A. Zisserman, “VGGFace2: A dataset for recognising faces across pose and age,” in FG
2018
Earlier work this paper cites.
F. Wang, L. Chen, C. Li, S. Huang, Y. Chen, C. Qian, and C. C. Loy, “The devil of face recognition is in the noise,” in ECCV
2018
Earlier work this paper cites.
X. Zhang, L. Yang, J. Yan, and D. Lin, “Accelerated training for massive classification via dynamic class selection,” in AAAI
2018
Earlier work this paper cites.
E. Smirnov, A. Melnikov, A. Oleinik, E. Ivanova, I. Kalinovskiy, and E. Luckyanets, “Hard example mining with auxiliary embeddings,” in CVPR Workshops
2018
Earlier work this paper cites.
F. Wang, J. Cheng, W. Liu, and H. Liu, “Additive margin softmax for face verification,” IEEE Signal Processing Letters
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
M. Wydmuch, K. Jasinska, M. Kuznetsov, R. Busa-Fekete, and K. Dembczyński, “A no-regret generalization of hierarchical softmax to extreme multi-label classification,” in NeurIPS
2018
Earlier work this paper cites.
G. Blanc and S. Rendle, “Adaptive sampled softmax with kernel based sampling,” in ICML
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
H. Qi, M. Brown, and D. G. Lowe, “Low-shot learning with imprinted weights,” in CVPR
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
Y. Zheng, D. K. Pal, and M. Savvides, “Ring loss: Convex feature normalization for face recognition,” in CVPR
2018
Earlier work this paper cites.
Y. Qing, Y. Zhao, Y. Shi, D. Chen, Y. Lin, and Y. Peng, “Improve cross-domain face recognition with IBN-block,” in Big Data
2018
Earlier work this paper cites.
X. Zhan, Z. Liu, J. Yan, D. Lin, and C. C. Loy, “Consensus-driven propagation in massive unlabeled data for face recognition,” in ECCV
2018
Earlier work this paper cites.
X. Wu, R. He, Z. Sun, and T. Tan, “A Light CNN for deep face representation with noisy labels,” IEEE Transactions on Information Forensics and Security
2018
Cited alongside, same era.
J. Deng, J. Guo, N. Xue, and S. Zafeiriou, “ArcFace: Additive angular margin loss for deep face recognition,” in CVPR
2019
Cited alongside, same era.
M. Wang, W. Deng, J. Hu, X. Tao, and Y. Huang, “Racial faces in the wild: Reducing racial bias by information maximization adaptation network,” in ICCV
2019
Cited alongside, same era.
M. Singh, R. Singh, M. Vatsa, N. K. Ratha, and R. Chellappa, “Recognizing disguised faces in the wild,” IEEE Transactions on Biometrics, Behavior, and Identity Science
2019
Cited alongside, same era.
H. Liu, X. Zhu, Z. Lei, and S. Z. Li, “AdaptiveFace: Adaptive margin and sampling for face recognition,” in CVPR
2019
M. Dukhan and A. Ablavatski, “Two-pass softmax algorithm,” in IPDPSW
2020
Later among the works it cites.
R. Bamler and S. Mandt, “Extreme classification via adversarial softmax approximation,” in ICLR
2020
Later among the works it cites.
X. Zhang, R. Zhao, Y. Qiao, and H. Li, “RBF-Softmax: Learning deep representative prototypes with radial basis function softmax,” in ECCV
2020
Later among the works it cites.
X. Wang, H. Zhang, W. Huang, and M. R. Scott, “Cross-batch memory for embedding learning,” in CVPR
2020
Later among the works it cites.
2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Y. Duan, J. Lu, and J. Zhou, “UniformFace: Learning deep equidistributed representation for face recognition,” in CVPR
2019
Cited alongside, same era.
K. Zhao, J. Xu, and M.-M. Cheng, “RegularFace: Deep face recognition via exclusive regularization,” in CVPR
2019
Cited alongside, same era.
B. Liu, W. Deng, Y. Zhong, M. Wang, J. Hu, X. Tao, and Y. Huang, “Fair loss: Margin-aware reinforcement learning for deep face recognition,” in ICCV
2019
Cited alongside, same era.
E. Smirnov, A. Oleinik, A. Lavrentev, E. Shulga, V. Galyuk, N. Garaev, M. Zakuanova, and A. Melnikov, “Face representation learning using composite mini-batches,” in ICCV Workshops
2019
Cited alongside, same era.
S. J. Reddi, S. Kale, F. Yu, D. Holtmann-Rice, J. Chen, and S. Kumar, “Stochastic negative mining for learning with large output spaces,” in AISTATS
2019
Cited alongside, same era.
A. S. Rawat, J. Chen, F. X. X. Yu, A. T. Suresh, and S. Kumar, “Sampled softmax with random fourier features,” in NeurIPS
2019
Cited alongside, same era.
Z. Zhong, L. Zheng, Z. Luo, S. Li, and Y. Yang, “Invariance matters: Exemplar memory for domain adaptive person re-identification,” in CVPR
2019
Cited alongside, same era.
Q. Wang, Y. Ma, K. Zhao, and Y. Tian, “A comprehensive survey of loss functions in machine learning,” Annals of Data Science
2020
Later among the works it cites.
2020
Later among the works it cites.
X. Wu, R. He, Y. Hu, and Z. Sun, “Learning an evolutionary embedding via massive knowledge distillation,” International Journal of Computer Vision
2020
Later among the works it cites.
C. Dulhanty and A. Wong, “Investigating the impact of inclusion in face recognition training data on individual face identification,” in AEIS
2020
Later among the works it cites.
Q. Wang, T. Wu, H. Zheng, and G. Guo, “Hierarchical pyramid diverse attention networks for face recognition,” in CVPR
2020
Later among the works it cites.
H. Zhao, Y. Shi, X. Tong, X. Ying, and H. Zha, “QAMFace: Quadratic additive angular margin loss for face recognition,” in ICIP
2020
Later among the works it cites.
S. Yang, W. Deng, M. Wang, J. Du, and J. Hu, “Orthogonality loss: Learning discriminative representations for face recognition,” IEEE Transactions on Circuits and Systems for Video Technology
2020
Later among the works it cites.
I. Kim, S. Han, S.-J. Park, J.-W. Baek, J. Shin, J.-J. Han, and C. Choi, “DiscFace: Minimum discrepancy learning for deep face recognition,” in ACCV
2020
Later among the works it cites.
G. Wang, L. Chen, T. Liu, M. He, and J. Luo, “DAIL: Dataset-aware and invariant learning for face recognition,” in ICPR
2020
Later among the works it cites.
Y. Huang, P. Shen, Y. Tai, S. Li, X. Liu, J. Li, F. Huang, and R. Ji, “Improving face recognition from hard samples via distribution distillation loss,” in ECCV
2020
Later among the works it cites.
A. Ali, M. Testa, T. Bianchi, and E. Magli, “BioMetricNet: deep unconstrained face verification through learning of metrics regularized onto gaussian distributions,” in ECCV
2020
Later among the works it cites.
H. Du, H. Shi, Y. Liu, J. Wang, Z. Lei, D. Zeng, and T. Mei, “Semi-siamese training for shallow face learning,” in ECCV
2020
Later among the works it cites.
X. Wang, S. Wang, C. Chi, S. Zhang, and T. Mei, “Loss function search for face recognition,” in ICML
2020
Later among the works it cites.
A. Buslaev, V. I. Iglovikov, E. Khvedchenya, A. Parinov, M. Druzhinin, and A. A. Kalinin, “Albumentations: Fast and flexible image augmentations,” Information
2020
Later among the works it cites.
S. B. Ahmed, S. F. Ali, J. Ahmad, M. Adnan, and M. M. Fraz, “On the frontiers of pose invariant face recognition: a review,” Artificial Intelligence Review
2020
Later among the works it cites.
T. Sixta, J. C. J. Junior, P. Buch-Cardona, E. Vazquez, and S. Escalera, “FairFace challenge at ECCV 2020: Analyzing bias in face recognition,” in ECCV
2020
Later among the works it cites.
Y. Zhang and W. Deng, “Class-balanced training for deep face recognition,” in CVPR Workshops
2020
Later among the works it cites.
W.-F. Ou, L.-M. Po, C. Zhou, Y.-J. Zhang, L.-T. Feng, Y. A. U. Rehman, and Y.-Z. Zhao, “LinCos-Softmax: Learning angle-discriminative face representations with linearity-enhanced cosine logits,” IEEE Access
2020
Later among the works it cites.
S. M. Iranmanesh, A. Dabouei, and N. M. Nasrabadi, “Attribute adaptive margin softmax loss using privileged information,” in BMVC
2020
Later among the works it cites.
J. Chang, Z. Lan, C. Cheng, and Y. Wei, “Data uncertainty learning in face recognition,” in CVPR
2020
Later among the works it cites.
Y. Sun, C. Cheng, Y. Zhang, C. Zhang, L. Zheng, Z. Wang, and Y. Wei, “Circle loss: A unified perspective of pair similarity optimization,” in CVPR
2020
Later among the works it cites.
2020
Later among the works it cites.
A. Dabouei, F. Taherkhani, S. Soleymani, J. Dawson, and N. M. Nasrabadi, “Boosting deep face recognition via disentangling appearance and geometry,” in WACV
2020
Later among the works it cites.
J. Yang, A. Bulat, and G. Tzimiropoulos, “FAN-Face: a simple orthogonal improvement to deep face recognition,” in AAAI
2020
Later among the works it cites.
I. Masi, Y. Wu, T. Hassner, and P. Natarajan, “Deep face recognition: A survey,” Neurocomputing
2021
Closest in time.
2021
Closest in time.
Z. Zhu, G. Huang, J. Deng, Y. Ye, J. Huang, X. Chen, J. Zhu, T. Yang, J. Lu, D. Du, and J. Zhou, “WebFace260M: A benchmark unveiling the power of million-scale deep face recognition,” in CVPR
2021
Closest in time.
Q. Meng, S. Zhao, Z. Huang, and F. Zhou, “MagFace: A universal representation for face recognition and quality assessment,” in CVPR
2021
Closest in time.
2021
Closest in time.
X. Xu, Q. Meng, Y. Qin, J. Guo, C. Zhao, F. Zhou, and Z. Lei, “Searching for alignment in face recognition,” in AAAI
2021
Closest in time.
2021
Closest in time.
J. Li, P. Zhou, C. Xiong, and S. C. Hoi, “Prototypical contrastive learning of unsupervised representations,” in ICLR
2021
Closest in time.
M. De Lange and T. Tuytelaars, “Continual prototype evolution: Learning online from non-stationary data streams,” in ICCV
2021
Closest in time.
J. Gou, B. Yu, S. J. Maybank, and D. Tao, “Knowledge distillation: A survey,” International Journal of Computer Vision
2021
Closest in time.
Y. Zhong, W. Deng, J. Hu, D. Zhao, X. Li, and D. Wen, “SFace: Sigmoid-constrained hypersphere loss for robust face recognition,” IEEE Transactions on Image Processing
2021
Closest in time.
J. Xu, T. Guo, Y. Xu, Z. Xu, and K. Bai, “MultiFace: A generic training mechanism for boosting face recognition performance,” Neurocomputing
2021
Closest in time.
2021
Closest in time.
Z. Huang, J. Zhang, and H. Shan, “When age-invariant face recognition meets face age synthesis: A multi-task learning framework,” in CVPR
2021
Closest in time.
2021
Closest in time.
A. Sanakoyeu, P. Ma, V. Tschernezki, and B. Ommer, “Improving deep metric learning by divide and conquer,” IEEE Transactions on Pattern Analysis and Machine Intelligence
2021
Closest in time.
L. S. Luevano, L. Chang, H. Méndez-Vázquez, Y. Martínez-Díaz, and M. González-Mendoza, “A study on the performance of unconstrained very low resolution face recognition: Analyzing current trends and new research directions,” IEEE Access
2021
Closest in time.