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Related Questions
- Can synthetic negative examples lead to overfitting to the augmentation process?
- How might the quality of synthetic negative examples impact the overall model performance?
- Can relying too heavily on synthetic negative examples lead to a lack of diversity in the training data?
- How might the generation process of synthetic negative examples affect the model's ability to generalize?
- Can the use of synthetic negative examples introduce bias into the model, if the augmentation process is biased?
- How might the ratio of synthetic to real negative examples impact the model's performance?
- Can the reliance on synthetic negative examples lead to a decrease in the model's robustness to out-of-distribution data?
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