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Related Questions
- How does few-shot learning compare to traditional machine learning methods in terms of data requirements?
- What are the key challenges in adapting few-shot learning models to new, unseen data distributions?
- Can few-shot learning models learn to recognize patterns in data that are not present in the training set?
- How does the concept of 'distribution shift' impact the performance of few-shot learning models?
- What are some strategies for improving the robustness of few-shot learning models to unseen data distributions?
- Can few-shot learning models be used for out-of-distribution (OOD) detection, and if so, how?
- What are the implications of few-shot learning models' limitations on generalizability for real-world applications?
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