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Related Questions
- How can I apply data augmentation techniques to enhance the diversity of my dataset and improve its robustness to outliers and anomalies?
- What are some common data augmentation methods I can use to generate synthetic data that mimics real-world scenarios?
- Can I use generative models like GANs or VAEs to create realistic synthetic data for my model?
- How can I determine the optimal level of data augmentation to apply to my model without overfitting or underfitting?
- What are some strategies for evaluating the effectiveness of data augmentation and synthetic data generation on my model's performance?
- Can I use transfer learning to leverage pre-trained models and fine-tune them on my augmented or synthetic data?
- How can I ensure that my synthetic data is diverse and representative of the real-world data distribution, and not biased towards any particular class or scenario?
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