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Modeling data uncertainty is important for noisy images, but seldom explored for face recognition.
Estimating the mean and variance of the target probability distribution
David A Nix and Andreas S Weigend · 1994
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Uncertainties in risk analysis: Six levels of treatment
M Elisabeth Paté-Cornell · 1996
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Regression with input-dependent noise: A bayesian treatment
Christopher M Bishop and Cazhaow S Quazaz · 1997
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Paul W Goldberg, Christopher KI Williams, and Christopher M Bishop · 1998
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On the treatment of uncertainties and probabilities in engineering decision analysis
Michael Havbro Faber · 2005
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Heteroscedastic gaussian process regression
Quoc V Le, Alex J Smola, and Stéphane Canu · 2005
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Labeled faces in the wild: A database forstudying face recognition in unconstrained environments
Gary B Huang, Marwan Mattar, Tamara Berg, and Eric Learned-Miller · 2008
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Aleatory or epistemic? does it matter?
Armen Der Kiureghian and Ove Ditlevsen · 2009
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Face recognition in unconstrained videos with matched background similarity
Lior Wolf, Tal Hassner, and Itay Maoz · 2011
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2014
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A data-driven approach to cleaning large face datasets
Hong-Wei Ng and Stefan Winkler · 2014
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Learning face representation from scratch
Dong Yi, Zhen Lei, Shengcai Liao, and Stan Z Li · 2014
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Weight uncertainty in neural networks
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra · 2015
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Bayesian segnet: Model uncertainty in deep convolutional encoder-decoder architectures for scene understanding
Alex Kendall, Vijay Badrinarayanan, and Roberto Cipolla · 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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Deep learning and the information bottleneck principle
Naftali Tishby and Noga Zaslavsky · 2015
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Uncertainty in deep learning
Yarin Gal · 2016
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani · 2016
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Ms-celeb-1m: A dataset and benchmark for large-scale face recognition
Yandong Guo, Lei Zhang, Yuxiao Hu, Xiaodong He, and Jianfeng Gao · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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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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Frontal to profile face verification in the wild
Soumyadip Sengupta, Jun-Cheng Chen, Carlos Castillo, Vishal M Patel, Rama Chellappa, and David W Jacobs · 2016
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Squeeze-and-excitation networks
Jie Hu, Li Shen, and Gang Sun · 2018
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Iarpa janus benchmark-c: Face dataset and protocol
Brianna Maze, Jocelyn Adams, James A Duncan, Nathan Kalka, Tim Miller, Charles Otto, Anil K Jain, W Tyler Niggel, Janet Anderson, Jordan Cheney, et al · 2018
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The devil of face recognition is in the noise
Fei Wang, Liren Chen, Cheng Li, Shiyao Huang, Yanjie Chen, Chen Qian, and Chen Change Loy · 2018
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Additive margin softmax for face verification
Feng Wang, Jian Cheng, Weiyang Liu, and Haijun Liu · 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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A light cnn for deep face representation with noisy labels
Xiang Wu, Ran He, Zhenan Sun, and Tieniu Tan · 2018
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A discriminative feature learning approach for deep face recognition
Yandong Wen, Kaipeng Zhang, Zhifeng Li, and Yu Qiao · 2016
Cited alongside, same era.
Deep variational information bottleneck
Alexander A Alemi, Ian Fischer, Joshua V Dillon, and Kevin Murphy · 2017
Cited alongside, same era.
On the capacity of face representation
Sixue Gong, Vishnu Naresh Boddeti, and Anil K Jain · 2017
Cited alongside, same era.
Deep convolutional encoder-decoder network with model uncertainty for semantic segmentation
Shuya Isobe and Shuichi Arai · 2017
Cited alongside, same era.
What uncertainties do we need in bayesian deep learning for computer vision?
Alex Kendall and Yarin Gal · 2017
Cited alongside, same era.
Sphereface: Deep hypersphere embedding for face recognition
Weiyang Liu, Yandong Wen, Zhiding Yu, Ming Li, Bhiksha Raj, and Le Song · 2017
Cited alongside, same era.
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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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Towards interpretable face recognition
Bangjie Yin, Luan Tran, Haoxiang Li, Xiaohui Shen, and Xiaoming Liu · 2018
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Gaussian yolov3: An accurate and fast object detector using localization uncertainty for autonomous driving
Jiwoong Choi, Dayoung Chun, Hyun Kim, and Hyuk-Jae Lee · 2019
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Arcface: Additive angular margin loss for deep face recognition
Jiankang Deng, Jia Guo, Niannan Xue, and Stefanos Zafeiriou · 2019
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Noise-tolerant paradigm for training face recognition cnns
Wei Hu, Yangyu Huang, Fan Zhang, and Ruirui Li · 2019
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Striking the right balance with uncertainty
Salman Khan, Munawar Hayat, Syed Waqas Zamir, Jianbing Shen, and Ling Shao · 2019
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Uncertainty estimation in one-stage object detection
Florian Kraus and Klaus Dietmayer · 2019
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Probabilistic face embeddings
Yichun Shi, Anil K Jain, and Nathan D Kalka · 2019
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Robust person re-identification by modelling feature uncertainty
Tianyuan Yu, Da Li, Yongxin Yang, Timothy M Hospedales, and Tao Xiang · 2019
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Face recognition with bayesian convolutional networks for robust surveillance systems
Umara Zafar, Mubeen Ghafoor, Tehseen Zia, Ghufran Ahmed, Ahsan Latif, Kaleem Razzaq Malik, and Abdullahi Mohamud Sharif · 2019
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