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Related Questions
- What role does data augmentation play in improving model robustness, and how does it enhance generalizability?
- In the context of few-shot learning, how does data augmentation help alleviate the curse of dimensionality and improve transferability of knowledge?
- How does the choice of data augmentation technique, such as rotation or scaling, affect the model's ability to generalize to new, unseen data?
- What is the impact of over-augmentation on model generalizability, and how can this be avoided in practice?
- Can data augmentation be used to simulate the effects of domain shifts and improve a model's robustness to distributional shifts?
- In the context of few-shot learning, how does data augmentation interact with other regularization techniques, such as dropout and L1/L2 regularization?
- What is the relationship between data augmentation and meta-learning, and how can data augmentation be used to improve the generalizability of meta-learned models?
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