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Related Questions
- What are the implications of using more realistic synthetic negative examples on the accuracy of a machine learning model?
- How does the quality of synthetic negative examples impact the generalizability of a model to real-world scenarios?
- What are some strategies for balancing the trade-off between generating more realistic synthetic examples and the associated computational costs?
- Can you discuss the relationship between the complexity of the model architecture and the need for high-quality synthetic negative examples?
- How do the benefits of increased model robustness and generalizability from using high-quality synthetic examples compare to the costs of increased computational resources?
- What are some techniques for reducing the computational resources required to generate high-quality synthetic negative examples?
- What are the potential consequences of using low-quality synthetic negative examples on the performance of a machine learning model?
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