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Related Questions
- What are the key challenges in tasks with limited labeled data, and how does few-shot learning address them?
- How does few-shot learning leverage prior knowledge and experience to improve performance in new tasks?
- What are the differences between few-shot learning and traditional transfer learning, and when should each be used?
- Can few-shot learning be applied to tasks with very small datasets, and what are the potential limitations?
- How does few-shot learning handle the problem of overfitting, and what techniques can be used to mitigate it?
- What are the potential applications of few-shot learning in real-world scenarios, such as in robotics or natural language processing?
- How does few-shot learning compare to other transfer learning methods, such as meta-learning or multi-task learning, in terms of performance and efficiency?
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