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Related Questions
- How can the quality of synthetic negative examples affect the model's ability to handle out-of-distribution data?
- What are the potential consequences of using low-quality synthetic negative examples in model training?
- Can you explain the relationship between the diversity of synthetic negative examples and the model's robustness?
- How does the similarity between synthetic and real-world data impact the model's generalizability?
- What are the best practices for generating high-quality synthetic negative examples?
- Can you discuss the role of synthetic negative examples in reducing overfitting and improving model robustness?
- How can the quality of synthetic negative examples be evaluated, and what metrics can be used to measure it?
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