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Convolutional neural networks (CNNs) have achieved a great success in face recognition, which unfortunately comes at the cost of massive computation and storage consumption.
Model compression
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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
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Fitnets: Hints for thin deep nets
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Deepface: Closing the gap to human-level performance in face verification
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Distilling the knowledge in a neural network
Hinton, G., Vinyals, O., and Dean, J · 2015
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Deep face recognition
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Facenet: A unified embedding for face recognition and clustering
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Person re-identification by multi-channel parts-based cnn with improved triplet loss function
Cheng, D., Gong, Y., Zhou, S., Wang, J., and Zheng, N · 2016
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Ms-celeb-1m: A dataset and benchmark for large-scale face recognition
Guo, Y., Zhang, L., Hu, Y., He, X., and Gao, J · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Face model compression by distilling knowledge from neurons
Luo, P., Zhu, Z., Liu, Z., Wang, X., and Tang, X · 2016
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Deep model compression: Distilling knowledge from noisy teachers
Sau, B. B. and Balasubramanian, V. N · 2016
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Improved deep metric learning with multi-class n-pair loss objective
Sohn, K · 2016
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Zagoruyko, S. and Komodakis, N · 2016
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Joint face detection and alignment using multitask cascaded convolutional networks
Zhang, K., Zhang, Z., Li, Z., and Qiao, Y · 2016
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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
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3d object instance recognition and pose estimation using triplet loss with dynamic margin
Zakharov, S., Kehl, W., Planche, B., Hutter, A., and Ilic, S · 2017
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Arcface: Additive angular margin loss for deep face recognition
Deng, J., Guo, J., Xue, N., and Zafeiriou, S · 2018
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Triplet loss in siamese network for object tracking
Dong, X. and Shen, J · 2018
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Furlanello, T., Lipton, Z. C., Tschannen, M., Itti, L., and Anandkumar, A · 2018
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Beyond triplet loss: a deep quadruplet network for person re-identification
Chen, W., Chen, X., Zhang, J., and Huang, K · 2017
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Efficient knowledge distillation from an ensemble of teachers
Fukuda, T., Suzuki, M., Kurata, G., Thomas, S., Cui, J., and Ramabhadran, B · 2017
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In defense of the triplet loss for person re-identification
Hermans, A., Beyer, L., and Leibe, B · 2017
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Like what you like: Knowledge distill via neuron selectivity transfer
Huang, Z. and Wang, N · 2017
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Simple triplet loss based on intra/inter-class metric learning for face verification
Ming, Z., Chazalon, J., Luqman, M. M., Visani, M., and Burie, J.-C · 2017
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Learning student networks via feature embedding
Chen, H., Wang, Y., Xu, C., Xu, C., and Tao, D
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Mobilefacenets: Efficient cnns for accurate real-time face verification on mobile devices
Chen, S., Liu, Y., Gao, X., and Han, Z
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Deep metric learning with hierarchical triplet loss
Ge, W · 2018
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Deep ranking model by large adaptive margin learning for person re-identification
Wang, J., Zhou, S., Wang, J., and Hou, Q · 2018
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Shift: A zero flop, zero parameter alternative to spatial convolutions
Wu, B., Wan, A., Yue, X., Jin, P., Zhao, S., Golmant, N., Gholaminejad, A., Gonzalez, J., and Keutzer, K · 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
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Rocket launching: A universal and efficient framework for training well-performing light net
Zhou, G., Fan, Y., Cui, R., Bian, W., Zhu, X., and Gai, K · 2018
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