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State-of-the-art deep face recognition methods are mostly trained with a softmax-based multi-class classification framework.
In defense of one-vs-all classification
Ryan Rifkin and Aldebaro Klautau · 2004
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Learning a similarity metric discriminatively, with application to face verification
Sumit Chopra, Raia Hadsell, and Yann LeCun · 2005
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Dimensionality reduction by learning an invariant mapping
Raia Hadsell, Sumit Chopra, and Yann LeCun · 2006
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Labeled faces in the wild: A database for studying face recognition in unconstrained environments
Gary B Huang, Manu Ramesh, Tamara Berg, and Erik Learned-Miller · 2007
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Deep sparse rectifier neural networks
Xavier Glorot, Antoine Bordes, and Yoshua Bengio · 2011
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Deep learning face representation by joint identification-verification
Yi Sun, Yuheng Chen, Xiaogang Wang, and Xiaoou Tang · 2014
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Deep learning face representation from predicting 10,000 classes
Yi Sun, Xiaogang Wang, and Xiaoou Tang · 2014
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Deepface: Closing the gap to human-level performance in face verification
Yaniv Taigman, Ming Yang, Marc’Aurelio Ranzato, and Lior Wolf · 2014
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Megaface: A million faces for recognition at scale
Daniel Miller, E Brossard, S Seitz, and I Kemelmacher-Shlizerman · 2015
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Deep face recognition
Omkar M Parkhi, Andrea Vedaldi, and Andrew Zisserman · 2015
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Facenet: A unified embedding for face recognition and clustering
Florian Schroff, Dmitry Kalenichenko, and James Philbin · 2015
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Deeply learned face representations are sparse, selective, and robust
Yi Sun, Xiaogang Wang, and Xiaoou Tang · 2015
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The megaface benchmark: 1 million faces for recognition at scale
Ira Kemelmacher-Shlizerman, Steven M. Seitz, Daniel Miller, and Evan Brossard · 2016
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Large-margin softmax loss for convolutional neural networks
Weiyang Liu, Yandong Wen, Zhiding Yu, and Meng Yang · 2016
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Deep metric learning via lifted structured feature embedding
Hyun Oh Song, Yu Xiang, Stefanie Jegelka, and Silvio Savarese · 2016
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Improved deep metric learning with multi-class n-pair loss objective
Kihyuk Sohn · 2016
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Learning deep embeddings with histogram loss
Evgeniya Ustinova and Victor Lempitsky · 2016
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A discriminative feature learning approach for deep face recognition
Yandong Wen, Kaipeng Zhang, Zhifeng Li, and Yu Qiao · 2016
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Joint face detection and alignment using multi-task cascaded convolutional networks
Kaipeng Zhang, Zhanpeng Zhang, Zhifeng Li, and Yu Qiao · 2016
Cited alongside, same era.
On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 2017
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Sphereface: Deep hypersphere embedding for face recognition
Weiyang Liu, Yandong Wen, Zhiding Yu, Ming Li, Bhiksha Raj, and Le Song · 2017
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Deep hyperspherical learning
Weiyang Liu, Yan-Ming Zhang, Xingguo Li, Zhiding Yu, Bo Dai, Tuo Zhao, and Le Song · 2017
Cited alongside, same era.
Learning deep features via congenerous cosine loss for person recognition
Yu Liu, Hongyang Li, and Xiaogang Wang · 2017
Cited alongside, same era.
Simple triplet loss based on intra/inter-class metric learning for face verification
Additive margin softmax for face verification
Feng Wang, Weiyang Liu, Haijun Liu, and Jian Cheng · 2018
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Cosface: Large margin cosine loss for deep face recognition
Hao Wang, Yitong Wang, Zheng Zhou, Xing Ji, Dihong Gong, Jingchao Zhou, Zhifeng Li, and Wei Liu · 2018
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Comparator networks
Weidi Xie, Li Shen, and Andrew Zisserman · 2018
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Multicolumn networks for face recognition
Weidi Xie and Andrew Zisserman · 2018
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Cross-pose lfw: A database for studying cross-pose face recognition in unconstrained environments
Tianyue Zheng and Weihong Deng · 2018
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Zuheng Ming, Joseph Chazalon, Muhammad Muzzamil Luqman, Muriel Visani, and Jean-Christophe Burie · 2017
Cited alongside, same era.
Agedb: the first manually collected, in-the-wild age database
Stylianos Moschoglou, Athanasios Papaioannou, Christos Sagonas, Jiankang Deng, Irene Kotsia, and Stefanos Zafeiriou · 2017
Cited alongside, same era.
L2-constrained softmax loss for discriminative face verification
Rajeev Ranjan, Carlos D Castillo, and Rama Chellappa · 2017
Cited alongside, same era.
Normface: L2 hypersphere embedding for face verification
Feng Wang, Xiang Xiang, Jian Cheng, and Alan Loddon Yuille · 2017
Cited alongside, same era.
Deep metric learning with angular loss
Jian Wang, Feng Zhou, Shilei Wen, Xiao Liu, and Yuanqing Lin · 2017
Cited alongside, same era.
Iarpa janus benchmark-b face dataset
Cameron Whitelam, Emma Taborsky, Austin Blanton, Brianna Maze, Jocelyn Adams, Tim Miller, Nathan Kalka, Anil K Jain, James A Duncan, Kristen Allen, et al · 2017
Cited alongside, same era.
Sampling matters in deep embedding learning
Chao-Yuan Wu, R Manmatha, Alexander J Smola, and Philipp Krahenbuhl · 2017
Cited alongside, same era.
Yutong Zheng, Dipan K Pal, and Marios Savvides · 2018
Later among the works it cites.
Arcface: Additive angular margin loss for deep face recognition
Jiankang Deng, Jia Guo, Niannan Xue, and Stefanos Zafeiriou · 2019
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Multi-similarity loss with general pair weighting for deep metric learning
Xun Wang, Xintong Han, Weilin Huang, Dengke Dong, and Matthew R Scott · 2019
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A comprehensive study on center loss for deep face recognition
Yandong Wen, Kaipeng Zhang, Zhifeng Li, and Yu Qiao · 2019
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Adversarial learning with margin-based triplet embedding regularization
Yaoyao Zhong and Weihong Deng · 2019
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Partial fc: Training 10 million identities on a single machine
Xiang An, Xuhan Zhu, Yang Xiao, Lan Wu, Ming Zhang, Yuan Gao, Bin Qin, Debing Zhang, and Ying Fu · 2020
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Curricularface: adaptive curriculum learning loss for deep face recognition
Yuge Huang, Yuhan Wang, Ying Tai, Xiaoming Liu, Pengcheng Shen, Shaoxin Li, Jilin Li, and Feiyue Huang · 2020
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Regularizing neural networks via minimizing hyperspherical energy
Rongmei Lin, Weiyang Liu, Zhen Liu, Chen Feng, Zhiding Yu, James M Rehg, Li Xiong, and Le Song · 2020
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Circle loss: A unified perspective of pair similarity optimization
Yifan Sun, Changmao Cheng, Yuhan Zhang, Chi Zhang, Liang Zheng, Zhongdao Wang, and Yichen Wei · 2020
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Orthogonal over-parameterized training
Weiyang Liu, Rongmei Lin, Zhen Liu, James M Rehg, Liam Paull, Li Xiong, Le Song, and Adrian Weller · 2021
Closest in time.
Learning with hyperspherical uniformity
Weiyang Liu, Rongmei Lin, Zhen Liu, Li Xiong, Bernhard Schölkopf, and Adrian Weller · 2021
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Sphereface revived: Unifying hyperspherical face recognition
Weiyang Liu, Yandong Wen, Bhiksha Raj, Rita Singh, and Adrian Weller · 2022
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