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Deep neural networks have been widely used in numerous computer vision applications, particularly in face recognition.
Labeled faces in the wild: A database forstudying face recognition in unconstrained environments
G. B. Huang, M. Mattar, T. Berg, and E. Learned-Miller · 2008
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Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L. jia Li, K. Li, and L. Fei-fei · 2009
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Rich feature hierarchies for accurate object detection and semantic segmentation
R. Girshick, J. Donahue, T. Darrell, and J. Malik · 2014
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Very deep convolutional networks for large-scale image recognition, 2014
K. Simonyan and A. Zisserman · 2014
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Binaryconnect: Training deep neural networks with binary weights during propagations
M. Courbariaux, Y. Bengio, and J. David · 2015
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Beyond principal components: Deep boltzmann machines for face modeling
C. N. Duong, K. Luu, K. Quach, and T. Bui · 2015
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S. Han, H. Mao, and W. J. Dally · 2015
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Learning both weights and connections for efficient neural networks
S. Han, J. Pool, J. Tran, and W. J. Dally · 2015
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Facenet: A unified embedding for face recognition and clustering
F. Schroff, D. Kalenichenko, and J. Philbin · 2015
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Binarynet: Training deep neural networks with weights and activations constrained to +1 or -1
M. Courbariaux and Y. Bengio · 2016
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Longitudinal face modeling via temporal deep restricted boltzmann machines
C. N. Duong, K. Luu, K. Quach, and T. Bui · 2016
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Ms-celeb-1m: A dataset and benchmark for large-scale face recognition
Y. Guo, L. Zhang, Y. Hu, X. He, and J. Gao · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Binarized neural networks
I. Hubara, M. Courbariaux, D. Soudry, R. El-Yaniv, and Y. Bengio · 2016
Earlier work this paper cites.
The megaface benchmark: 1 million faces for recognition at scale
I. Kemelmacher-Shlizerman, S. M. Seitz, D. Miller, and E. Brossard · 2016
Cited alongside, same era.
Robust hand detection in vehicles
H. N. Le, C. Zhu, Y. Zheng, K. Luu, and M. Savvides · 2016
Cited alongside, same era.
Xnor-net: Imagenet classification using binary convolutional neural networks
M. Rastegari, V. Ordonez, J. Redmon, and A. Farhadi · 2016
Cited alongside, same era.
Joint face detection and alignment using multitask cascaded convolutional networks
K. Zhang, Z. Zhang, Z. Li, and Y. Qiao · 2016
Cited alongside, same era.
Towards a deep learning framework for unconstrained face detection
Y. Zheng, C. Zhu, K. Luu, H. N. Le, C. Bhagavatula, and M. Savvides · 2016
Cited alongside, same era.
Weakly supervised facial analysis with dense hyper-column features
C. Zhu, Y. Zheng, K. Luu, H. N. Le, C. Bhagavatula, and M. Savvides · 2016
Mobilefacenets: Efficient cnns for accurate real-time face verification on mobile devices
S. Chen, Y. Liu, X. Gao, and Z. Han · 2018
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Deep appearance models: A deep boltzmann machine approach for face modeling
C. N. Duong, K. Luu, K. Quach, and T. Bui · 2018
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Deep recurrent level set for segmenting brain tumors
M. S. H. N. Le, R. Gummadi · 2018
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Deep contextual recurrent residual networks for scene labeling
H. N. Le, C. N. Duong, K. Luu, and M. Savvides · 2018
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Reformulating level sets as deep recurrent neural network approach to semantic segmentation
H. N. Le, K. G. Quach, K. Luu, and M. Savvides · 2018
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Fd-mobilenet: Improved mobilenet with a fast downsampling strategy
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Cited alongside, same era.
Temporal non-volume preserving approach to facial age-progression and age-invariant face recognition
C. N. Duong, K. G. Quach, K. Luu, T. H. N. Le, and M. Savvides · 2017
Cited alongside, same era.
Mask R-CNN
K. He, G. Gkioxari, P. Dollár, and R. Girshick · 2017
Cited alongside, same era.
Mobilenets: Efficient convolutional neural networks for mobile vision applications
A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam · 2017
Cited alongside, same era.
Mobilenets: Efficient convolutional neural networks for mobile vision applications
A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam · 2017
Cited alongside, same era.
Densely connected convolutional networks
G. Huang, Z. Liu, L. van der Maaten, and K. Q. Weinberger · 2017
Cited alongside, same era.
Mimicking very efficient network for object detection
Q. Li, S. Jin, and J. Yan · 2017
Cited alongside, same era.
Z. Qin, Z. Zhang, X. Chen, C. Wang, and Y. Peng · 2018
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Mobilenetv2: Inverted residuals and linear bottlenecks
M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen · 2018
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M. Sandler, A. G. Howard, M. Zhu, A. Zhmoginov, and L. Chen · 2018
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Cosface: Large margin cosine loss for deep face recognition
H. Wang, Y. Wang, Z. Zhou, X. Ji, D. Gong, J. Zhou, Z. Li, and W. Liu · 2018
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Quantization mimic: Towards very tiny cnn for object detection
Y. Wei, X. Pan, H. Qin, and J. Yan · 2018
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A light cnn for deep face representation with noisy labels
X. Wu, R. He, Z. Sun, and T. Tan · 2018
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Seeing small faces from robust anchor’s perspective
C. Zhu, Y. Ran, K. Luu, and M. Savvides · 2018
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Enhancing interior and exterior deep facial features for face detection in the wild
C. Zhu, Y. Zheng, K. Luu, and M. Savvides · 2018
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