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Related Questions
- What is the difference between data augmentation and data synthesis in improving a model's ability to handle out-of-distribution data?
- Can you provide examples of effective data augmentation techniques for natural language processing tasks?
- How does data augmentation impact the generalizability of a model to unseen data?
- What are some common pitfalls to avoid when using data augmentation to improve a model's out-of-distribution handling?
- How can data augmentation be used in conjunction with other techniques, such as transfer learning and domain adaptation, to improve a model's ability to handle out-of-distribution data?
- What are some real-world applications of data augmentation in improving a model's ability to handle out-of-distribution data?
- Can you explain the concept of 'data augmentation' and how it helps in reducing overfitting in deep learning models?
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