Summary of Fairdd: Enhancing Fairness with Domain-incremental Learning in Dermatological Disease Diagnosis, by Yiqin Luo et al.
FairDD: Enhancing Fairness with domain-incremental learning in dermatological disease diagnosis
by Yiqin Luo, Tianlong Gu
First submitted to arxiv on: 21 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Computers and Society (cs.CY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel approach for achieving a better balance between accuracy and fairness in dermatological diagnostic models, leveraging domain incremental learning to address decision bias. The FairDD network uses mixup data augmentation and supervised contrastive learning to enhance robustness and generalization, demonstrating improved performance on two dermatological datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers aim to improve the trade-off between accuracy and fairness in dermatological disease diagnosis using deep learning models. They propose a new approach called FairDD, which uses domain incremental learning to balance learning across different groups by detecting changes in data distribution. This method also incorporates mixup data augmentation and supervised contrastive learning to boost robustness and generalization. The results show that FairDD outperforms existing methods in fairness criteria and the trade-off between fairness and performance. |
Keywords
» Artificial intelligence » Data augmentation » Deep learning » Generalization » Supervised
