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Related Questions
- How do active learning and semi-supervised learning approaches differ in terms of their reliance on human annotation?
- What are the key characteristics of active learning that enable it to be more efficient than traditional supervised learning in terms of labeled data?
- Can you explain the concept of curiosity-driven sampling in active learning and its role in reducing the need for labeled data?
- What are some common applications of semi-supervised learning where the availability of labeled data is limited?
- How do semi-supervised learning algorithms leverage unlabeled data to improve the performance of machine learning models?
- What are some challenges associated with implementing active learning in real-world scenarios, and how can they be addressed?
- Can you describe a scenario where active learning would be more suitable than semi-supervised learning, and vice versa?
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