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Related Questions
- Can GANs be used to generate synthetic data for rare or hard-to-obtain scenarios, improving model performance in those areas?
- How can GANs be employed to create diverse and realistic training data, reducing overfitting and improving model generalizability?
- Can GANs be used to generate data that mimics the distribution of real-world data, allowing models to learn from a more diverse set of examples?
- How can the generated data from GANs be used to augment existing training datasets, improving model performance on unseen data?
- Can GANs be used to generate data that highlights edge cases or corner cases, helping models to learn from more robust and diverse training data?
- How can the use of GANs for data generation impact the need for human labeling and annotation in machine learning pipelines?
- Can GANs be used to generate data that simulates real-world scenarios, allowing models to learn from more realistic and complex training data?
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