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Related Questions
- What are some common fairness techniques that can be combined with data augmentation to improve model fairness and robustness?
- How does data augmentation impact the performance of fairness metrics such as demographic parity and equal opportunity?
- Can you provide examples of data augmentation techniques that can be used to mitigate bias in machine learning models?
- How can data augmentation be used to improve the robustness of models to adversarial attacks?
- What are some challenges and limitations of combining data augmentation with other fairness techniques?
- Can you discuss the trade-offs between data augmentation and other fairness techniques, such as regularization and debiasing methods?
- How can data augmentation be used to improve the fairness and robustness of models in real-world applications, such as natural language processing and computer vision?
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