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Related Questions
- What are some common data augmentation techniques used to prevent overfitting in pre-trained models?
- Can you provide examples of how data augmentation can be applied to text, image, and audio data to prevent overfitting?
- How does data augmentation impact the performance of pre-trained models in real-world applications?
- What are the potential risks of over-reliance on data augmentation in preventing overfitting?
- Can you explain the role of data augmentation in transfer learning and fine-tuning pre-trained models?
- How does data augmentation compare to other regularization techniques, such as dropout and L1/L2 regularization, in preventing overfitting?
- What are some best practices for implementing data augmentation to prevent overfitting in pre-trained models?
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