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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.13061 |
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| _version_ | 1866909113181536256 |
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| author | Chung, Hao-Wei Chiu, Ching-Hao Chen, Yu-Jen Shi, Yiyu Ho, Tsung-Yi |
| author_facet | Chung, Hao-Wei Chiu, Ching-Hao Chen, Yu-Jen Shi, Yiyu Ho, Tsung-Yi |
| contents | Fairness has become increasingly pivotal in machine learning for high-risk applications such as machine learning in healthcare and facial recognition. However, we see the deficiency in the previous logits space constraint methods. Therefore, we propose a novel framework, Logits-MMD, that achieves the fairness condition by imposing constraints on output logits with Maximum Mean Discrepancy. Moreover, quantitative analysis and experimental results show that our framework has a better property that outperforms previous methods and achieves state-of-the-art on two facial recognition datasets and one animal dataset. Finally, we show experimental results and demonstrate that our debias approach achieves the fairness condition effectively. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_13061 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Toward Fairness via Maximum Mean Discrepancy Regularization on Logits Space Chung, Hao-Wei Chiu, Ching-Hao Chen, Yu-Jen Shi, Yiyu Ho, Tsung-Yi Computer Vision and Pattern Recognition Fairness has become increasingly pivotal in machine learning for high-risk applications such as machine learning in healthcare and facial recognition. However, we see the deficiency in the previous logits space constraint methods. Therefore, we propose a novel framework, Logits-MMD, that achieves the fairness condition by imposing constraints on output logits with Maximum Mean Discrepancy. Moreover, quantitative analysis and experimental results show that our framework has a better property that outperforms previous methods and achieves state-of-the-art on two facial recognition datasets and one animal dataset. Finally, we show experimental results and demonstrate that our debias approach achieves the fairness condition effectively. |
| title | Toward Fairness via Maximum Mean Discrepancy Regularization on Logits Space |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2402.13061 |