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Training face recognition models with millions of identities is challenging because classifier storage, logit memory, and computation grow linearly with the number of classes, eventually making full softmax impractical even when the backbone itself fits comfortably in memory.
Classes for fast maximum entropy training
Goodman, J. 2001 · 2001
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AgeDB: The First Manually Collected, In-the-Wild Age Database
Moschoglou, S.; Papaioannou, A.; Sagonas, C.; Deng, J.; Kotsia, I.; and Zafeiriou, S. 2017 · 2005
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Labeled Faces in the Wild: A Database forStudying Face Recognition in Unconstrained Environments
Huang, G. B.; Mattar, M.; Berg, T.; and Learned-Miller, E. 2008 · 2008
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Cross-Age LFW: A Database for Studying Cross-Age Face Recognition in Unconstrained Environments
Zheng, T.; Deng, W.; and Hu, J. 2017 · 2010
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Scaling distributed machine learning with the parameter server
Li, M.; Andersen, D. G.; Park, J. W.; Smola, A. J.; Ahmed, A.; Josifovski, V.; Long, J.; Shekita, E. J.; and Su, B.-Y. 2014 · 2014
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Deep Learning Face Attributes in the Wild
Liu, Z.; Luo, P.; Wang, X.; and Tang, X. 2015 · 2015
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FaceNet: A unified embedding for face recognition and clustering
Schroff, F.; Kalenichenko, D.; and Philbin, J. 2015 · 2015
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FACE AUTHENTICATION METHOD AND DEVICE
Wang, N.; Zhang, X.; Jiang, W.; and Zhang, Y. 2015 · 2015
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Deep Residual Learning for Image Recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
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Kemelmacher-Shlizerman, I.; Seitz, S. M.; Miller, D.; and Brossard, E. 2016 · 2016
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Frontal to profile face verification in the wild
Sengupta, S.; Chen, J.-C.; Castillo, C.; Patel, V. M.; Chellappa, R.; and Jacobs, D. W. 2016 · 2016
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Liu, W.; Wen, Y.; Yu, Z.; Li, M.; Raj, B.; and Song, L. 2017 · 2017
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Level Playing Field for Million Scale Face Recognition
Nech, A.; and Kemelmacher-Shlizerman, I. 2017 · 2017
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IARPA Janus Benchmark-B Face Dataset
Whitelam, C.; Taborsky, E.; Blanton, A.; Maze, B.; Adams, J.; Miller, T.; Kalka, N.; Jain, A. K.; Duncan, J. A.; Allen, K.; Cheney, J.; and Grother, P. 2017 · 2017
Accelerated Training for Massive Classification via Dynamic Class Selection
Zhang, X.; Yang, L.; Yan, J.; and Lin, D. 2018 · 2018
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Cross-pose LFW: A database for studying cross-pose face recognition in unconstrained environments
Zheng, T.; and Deng, W. 2018 · 2018
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ArcFace: Additive Angular Margin Loss for Deep Face Recognition
Deng, J.; Guo, J.; Xue, N.; and Zafeiriou, S. 2019 · 2019
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Softmax Dissection: Towards Understanding Intra- and Inter-class Objective for Embedding Learning
He, L.; Wang, Z.; Li, Y.; and Wang, S. 2020 · 2020
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CurricularFace: Adaptive Curriculum Learning Loss for Deep Face Recognition
Huang, Y.; Wang, Y.; Tai, Y.; Liu, X.; Shen, P.; Li, S.; Li, J.; and Huang, F. 2020 · 2020
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Celeb-500K: A Large Training Dataset for Face Recognition
Cao, J.; Li, Y.; and Zhang, Z. 2018 · 2018
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IARPA Janus Benchmark - C: Face Dataset and Protocol
Maze, B.; Adams, J.; Duncan, J. A.; Kalka, N.; Miller, T.; Otto, C.; Jain, A. K.; Niggel, W. T.; Anderson, J.; Cheney, J.; and Grother, P. 2018 · 2018
Cited alongside, same era.
Additive Margin Softmax for Face Verification
Wang, F.; Cheng, J.; Liu, W.; and Liu, H. 2018a
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CosFace: Large Margin Cosine Loss for Deep Face Recognition
Wang, H.; Wang, Y.; Zhou, Z.; Ji, X.; Gong, D.; Zhou, J.; Li, Z.; and Liu, W. 2018b
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Kim, Y.; Park, W.; Roh, M.-C.; and Shin, J. 2020 · 2020
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Circle Loss: A Unified Perspective of Pair Similarity Optimization
Sun, Y.; Cheng, C.; Zhang, Y.; Zhang, C.; Zheng, L.; Wang, Z.; and Wei, Y. 2020 · 2020
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