The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Deep Imbalanced Learning for Face Recognition and Attribute Prediction
Open accessAbstract
Data for face analysis often exhibit highly-skewed class distribution, i.e., most data belong to a few majority classes, while the minority classes only contain a scarce amount of instances. To mitigate this issue, contemporary deep learning methods typically follow classic strategies such as class re-sampling or cost-sensitive training. In this paper, we conduct extensive and systematic experiments to validate the effectiveness of these classic schemes for representation learning on class-imbalanced data. We further demonstrate that more discriminative deep representation can be learned by enforcing a deep network to maintain inter-cluster margins both within and between classes. This tight constraint effectively reduces the class imbalance inherent in the local data neighborhood, thus carving much more balanced class boundaries locally. We show that it is easy to deploy angular margins between the cluster distributions on a hypersphere manifold. Such learned Cluster-based Large Margin Local Embedding (CLMLE), when combined with a simple k-nearest cluster algorithm, shows significant improvements in accuracy over existing methods on both face recognition and face attribute prediction tasks that exhibit imbalanced class distribution.
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Cites
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FaceNet: A unified embedding for face recognition and clustering
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A Discriminative Feature Learning Approach for Deep Face Recognition
Wen, Y., Zhang, K., Li, Z., & Qiao, Y. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46478-7_31 · Cited by 4,077
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CosFace: Large Margin Cosine Loss for Deep Face Recognition
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PANDA: Pose Aligned Networks for Deep Attribute Modeling
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Training deep neural networks on imbalanced data sets
Wang, S., Liu, W., Wu, J., Cao, L., Meng, Q., & Kennedy, P. J. (2016) · 2016 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2016.7727770 · Cited by 491
Cited by
Past, 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
The class imbalance problem in deep learning
Ghosh, K., Bellinger, C., Corizzo, R., Branco, P., Krawczyk, B., & Japkowicz, N. (2024) · Machine Learning · doi:10.1007/s10994-022-06268-8 · Cited by 312
Learning From Multiple Experts: Self-paced Knowledge Distillation for Long-Tailed Classification
Xiang, L., Ding, G., & Han, J. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58558-7_15 · Cited by 268
Delving into deep imbalanced regression
Yang, Y., Zha, K., Chen, Y.-C., Wang, H., & Katabi, D. (2021) · arXiv · doi:10.48550/arxiv.2102.09554 · Cited by 229
Mitigating face recognition bias via group adaptive classifier
Gong, S., Liu, X., & Jain, A. K. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00342 · Cited by 92
Metadata
- Authors
- Huang, C., Li, Y., Loy, C. C., & Tang, X.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2020-11-01
- Volume
- 42
- Issue
- 11
- Pages
- 2781-2794
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- 1939-3539
- OpenAlex
- W2963212406
- Cited by
- 354
- References
- 7
- FWCI
- 21.56
- Type
- journal article
- Language
- en
- Open access
- green
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