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Related Questions
- What are the key differences between active learning and semi-supervised learning in the context of negative example generation?
- How can human evaluators effectively identify and label high-quality negative examples for machine learning models?
- Can you explain the concept of 'curated' negative examples and their role in improving model performance?
- What are some strategies for reducing the noise in negative examples collected through human-in-the-loop approaches?
- How can active learning be used to focus on the most uncertain or ambiguous examples in the dataset?
- What are some common pitfalls to avoid when implementing human-in-the-loop approaches for negative example generation?
- Can you describe a scenario where human-in-the-loop approaches were successfully used to improve the quality of negative examples in a large-scale machine learning project?
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