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Related Questions
- What are some limitations of few-shot learning when dealing with complex tasks or tasks with a large number of classes?
- How does the quality and diversity of the few-shot learning dataset impact the overall performance of the model?
- What are some common challenges when fine-tuning pre-trained models using few-shot learning for transfer learning?
- What are some potential biases or issues that can arise from using few-shot learning, particularly in cases where the dataset is imbalanced or incomplete?
- How can few-shot learning be applied to tasks with varying input modalities, such as text, images, and audio?
- What are some strategies for overcoming the overfitting issue that can occur when the number of training examples is very small?
- How can few-shot learning be used to adapt to changing environments or tasks where the distribution of the data is shifting over time?
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