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Related Questions
- What is the purpose of data augmentation in machine learning models and how does it relate to feature attribution methods?
- Can you explain how data augmentation affects the interpretability of feature attribution methods such as SHAP or LIME?
- How do feature attribution methods like DeepLIFT or SALiency Maps benefit from data augmentation in terms of improving their accuracy and reliability?
- What are the potential drawbacks of using data augmentation in conjunction with feature attribution methods, and how can they be mitigated?
- Can you discuss the role of data augmentation in identifying relevant features for model explainability and feature attribution?
- How do feature attribution methods like CAM (Class Activation Mapping) benefit from data augmentation in terms of highlighting important regions in the input data?
- What are the challenges of using data augmentation in conjunction with feature attribution methods in deep neural networks, and how can they be addressed?
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