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Related Questions
- What are some common limitations of few-shot learning in handling out-of-distribution data?
- How does the quality of the few-shot learning dataset impact the model's performance in real-world scenarios?
- What are some strategies for addressing the issue of overfitting in few-shot learning models?
- Can few-shot learning models generalize well to unseen tasks, and if so, what are the key factors that influence this ability?
- How does the choice of metric or evaluation criterion affect the performance of few-shot learning models in real-world applications?
- What are some potential sources of bias in few-shot learning datasets, and how can they be mitigated?
- Can few-shot learning models be used for transfer learning, and if so, what are the benefits and limitations of this approach?
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