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Related Questions
- Can data augmentation help reduce bias in machine learning models by increasing the representation of underrepresented groups?
- How does data augmentation affect the performance of models on minority groups, and are there any techniques to mitigate potential biases?
- What are some data augmentation techniques that can help improve model performance on underrepresented groups, such as oversampling, undersampling, or generating synthetic data?
- Can data augmentation be used to overcome class imbalance issues in underrepresented groups, and what are the implications for model performance?
- How does the quality of data augmentation impact model performance on underrepresented groups, and are there any best practices for data augmentation?
- Can data augmentation help improve model fairness by increasing the representation of underrepresented groups in the training data?
- What are some challenges associated with data augmentation for underrepresented groups, such as data quality, availability, and noise, and how can they be addressed?
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