The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
A Light CNN for Deep Face Representation With Noisy Labels
Open accessAbstract
The volume of convolutional neural network (CNN) models proposed for face recognition has been continuously growing larger to better fit the large amount of training data. When training data are obtained from the Internet, the labels are likely to be ambiguous and inaccurate. This paper presents a Light CNN framework to learn a compact embedding on the large-scale face data with massive noisy labels. First, we introduce a variation of maxout activation, called max-feature-map (MFM), into each convolutional layer of CNN. Different from maxout activation that uses many feature maps to linearly approximate an arbitrary convex activation function, MFM does so via a competitive relationship. MFM can not only separate noisy and informative signals but also play the role of feature selection between two feature maps. Second, three networks are carefully designed to obtain better performance, meanwhile, reducing the number of parameters and computational costs. Finally, a semantic bootstrapping method is proposed to make the prediction of the networks more consistent with noisy labels. Experimental results show that the proposed framework can utilize large-scale noisy data to learn a Light model that is efficient in computational costs and storage spaces. The learned single network with a 256-D representation achieves state-of-the-art results on various face benchmarks without fine-tuning.
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Cites
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ImageNet classification with deep convolutional neural networks
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FaceNet: A unified embedding for face recognition and clustering
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Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
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A Discriminative Feature Learning Approach for Deep Face Recognition
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Cited by
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
Deng, J., Guo, J., Xue, N., & Zafeiriou, S. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00482 · Cited by 7,781
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Self-citationPast, Present, and Future of Face Recognition: A Review
Adjabi, I., Ouahabi, A., Benzaoui, A., & Taleb-Ahmed, A. (2020) · Electronics · doi:10.3390/electronics9081188 · Cited by 560
Synthetic Data for Deep Learning
Nikolenko, S. I. (2021) · Springer Optimization and Its Applications · doi:10.1007/978-3-030-75178-4 · Cited by 423
AASIST: Audio Anti-Spoofing Using Integrated Spectro-Temporal Graph Attention Networks
Jung, J.-W., Heo, H.-S., Tak, H., Shim, H.-J., Chung, J. S., Lee, B.-J., Yu, H.-J., & Evans, N. (2022) · ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp43922.2022.9747766 · Cited by 399
Metadata
- Authors
- Wu, X., He, R., Sun, Z., & Tan, T.
- Venue
- IEEE Transactions on Information Forensics and Security
- Published
- 2018-11
- Volume
- 13
- Issue
- 11
- Pages
- 2884-2896
- ISSN
- 1556-6013 (ISSN-L)
- 1556-6021
- OpenAlex
- W2963460857
- Cited by
- 1,186
- References
- 10
- FWCI
- 83.87
- Type
- journal article
- Language
- en
- Open access
- green
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- Wu, X., He, R., Sun, Z., & Tan, T. (2018). A Light CNN for Deep Face Representation With Noisy Labels. IEEE Transactions on Information Forensics and Security. https://doi.org/10.1109/tifs.2018.2833032
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