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Related Questions
- Can you explain the concept of few-shot learning and why it's challenging to work with out-of-distribution data?
- What are some common challenges faced when adapting few-shot learning models to new, unseen data?
- How can data augmentation and regularization techniques help improve the robustness of few-shot learning models?
- What role does transfer learning play in improving the robustness of few-shot learning models to out-of-distribution data?
- Can you discuss the importance of selecting informative and diverse few-shot training examples?
- How can meta-learning techniques, such as model-agnostic meta-learning (MAML), help improve few-shot learning robustness?
- What are some strategies for handling epistemic uncertainty in few-shot learning models, particularly when dealing with out-of-distribution data?
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