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Related Questions
- What are some common methods for generating adversarial examples for training robust models?
- How does data augmentation with synthetic negative examples impact the accuracy of machine learning models?
- What are the differences between data augmentation, data synthesis, and data imputation in generating synthetic negative examples?
- Can you explain the concept of 'adversarial training' and its role in generating synthetic negative examples?
- What are some techniques for generating synthetic negative examples from existing positive examples, such as flipping, cropping, and color jittering?
- How can synthetic negative examples be used to improve the robustness of deep learning models to out-of-distribution data?
- What are the trade-offs between generating more realistic synthetic negative examples and the increased computational resources required?
- Can you discuss the limitations of using synthetic negative examples in cases where the underlying data distribution is complex or dynamic?
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