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Related Questions
- What are some common techniques used to select diverse and representative samples for active learning?
- How can we measure and evaluate the effectiveness of active learning in reducing bias and improving data diversity?
- What are some strategies for balancing the trade-off between the cost of labeling new samples and the potential benefits of improved data diversity?
- Can you discuss some challenges in implementing active learning in real-world settings, such as dealing with changing data distributions or uncertain outcomes?
- How can we incorporate domain knowledge and expert feedback into the active learning process to improve the quality of the selected samples?
- What are some methods for identifying and mitigating the effects of bias in the data or the active learning process itself?
- Can you explain how to integrate active learning with other machine learning techniques, such as transfer learning or ensemble methods, to further improve data diversity and reduce bias?
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