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Related Questions
- How does active learning select the most informative samples for labeling, and what are the key factors that influence this selection process?
- Can you explain the difference between uncertainty-based and query-based active learning, and how they address uncertainty and noise in the data?
- How does the level of noise in the data impact the performance of active learning algorithms, and what strategies can be employed to mitigate its effects?
- What are the implications of using active learning for model performance, particularly in terms of accuracy, robustness, and generalizability?
- How does active learning interact with other machine learning techniques, such as transfer learning and ensemble methods, to improve model performance?
- Can you discuss the role of human annotators in active learning, and how their biases and variability can impact the quality of the labeled data?
- What are some common challenges and limitations of active learning, and how can they be addressed through advances in algorithm design and data preprocessing?
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