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Related Questions
- How do ensemble methods like bagging and boosting address overfitting in few-shot learning models?
- Can combining the predictions of multiple models with different architectures reduce overfitting in few-shot learning scenarios?
- What are some strategies for using ensemble methods to improve the generalizability of few-shot learning models?
- How does the concept of 'ensemble diversity' impact the effectiveness of ensemble methods in reducing overfitting in few-shot learning?
- Can ensemble methods be used to leverage the strengths of different models and reduce overfitting in few-shot learning tasks?
- What are some common challenges associated with implementing ensemble methods in few-shot learning settings, and how can they be addressed?
- In what ways can ensemble methods be used to improve the robustness of few-shot learning models to data augmentation and other regularization techniques?
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