The DOI matches its Crossref record; 49 of the 50 references it cites were found in a registry or an index.
Feature Transfer Learning for Face Recognition With Under-Represented Data
Abstract
Despite the large volume of face recognition datasets, there is a significant portion of subjects, of which the samples are insufficient and thus under-represented. Ignoring such significant portion results in insufficient training data. Training with under-represented data leads to biased classifiers in conventionally-trained deep networks. In this paper, we propose a center-based feature transfer framework to augment the feature space of under-represented subjects from the regular subjects that have sufficiently diverse samples. A Gaussian prior of the variance is assumed across all subjects and the variance from regular ones are transferred to the under-represented ones. This encourages the under-represented distribution to be closer to the regular distribution. Further, an alternating training regimen is proposed to simultaneously achieve less biased classifiers and a more discriminative feature representation. We conduct ablative study to mimic the under-represented datasets by varying the portion of under-represented classes on the MS-Celeb-1M dataset. Advantageous results on LFW, IJB-A and MS-Celeb-1M demonstrate the effectiveness of our feature transfer and training strategy, compared to both general baselines and state-of-the-art methods. Moreover, our feature transfer successfully presents smooth visual interpolation, which conducts disentanglement to preserve identity of a class while augmenting its feature space with non-identity variations such as pose and lighting.
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In the citing paper
“When the data has a long-tail distribution, it can lead to bias in the classifier.”
From An Improved VGG19 Transfer Learning Strip Steel Surface Defect Recognition Deep Neural Network Based on Few Samples and Imbalanced Datasets (2021) · cites this paper as [4]
In the citing paper
“The classifier is more inclined to identify classes with a sufficient sample size and rich diversity [4,5].”
From An Improved VGG19 Transfer Learning Strip Steel Surface Defect Recognition Deep Neural Network Based on Few Samples and Imbalanced Datasets (2021) · cites this paper as [4]
Citations
Cites
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Flagged first, then by citations
1 of the 50 references couldn’t be matched to a known paper.
Research works printed but not linked: 5
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- lecun, 1998, The MNIST database of handwritten digits, Technical Report
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Cited by
Sorted by citations
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
Class-Balanced Loss Based on Effective Number of Samples
Cui, Y., Jia, M., Lin, T.-Y., Song, Y., & Belongie, S. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00949 · Cited by 3,094
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Decoupling Representation and Classifier for Long-Tailed RecognitionPreprint
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“variance (Yin et al., 2019) and transferring semantic deep features (Liu et al.”
BackgroundDeep Long-Tailed Learning: A Survey
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Metadata
- Authors
- Yin, X., Yu, X., Sohn, K., Liu, X., & Chandraker, M.
- Affiliations
- Microsoft (United States) (US)
- NEC (Japan) (JP)
- NEC (United States) (US)
- Michigan State University (US)
- University of California San Diego (US)
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 5697-5706
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2962895364
- Cited by
- 346
- References
- 50
- Citations by year
- 2026: 7 · 2025: 28 · 2024: 63 · 2023: 67 · 2022: 57 · 2021: 69 · 2020: 49 · 2019: 6
- FWCI
- 14.75
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- face recognition, underrepresented classes, classifier bias, alternating training, feature space augmentation, disentangled representations
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