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Related Questions
- How can active learning be used to reduce the need for human annotators in large-scale data annotation tasks?
- What strategies can be employed to select the most informative and diverse data points for annotation in active learning?
- Can active learning be used to identify and prioritize data points that require more nuanced annotation, such as context-dependent or ambiguous cases?
- How does active learning compare to other methods, such as transfer learning, in terms of improving model performance and reducing annotation effort?
- What are the key challenges in implementing active learning in real-world applications, and how can they be addressed?
- Can active learning be used to adapt to changing data distributions or concept drift, and if so, how?
- How can active learning be integrated with other AI techniques, such as reinforcement learning or transfer learning, to improve overall performance?
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