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Related Questions
- What are the key differences between active learning and human-in-the-loop methods, and how can they be combined to improve machine learning pipeline efficiency?
- How can active learning be used to identify the most informative samples for human annotation, and what are the benefits of this approach?
- What are some common human-in-the-loop methods used in machine learning, such as crowdsourcing and active learning, and how can they be integrated?
- How can the combination of active learning and human-in-the-loop methods help to reduce the need for large labeled datasets, and what are the implications for model development?
- What are some challenges and limitations of combining active learning and human-in-the-loop methods, and how can they be addressed?
- How can the active learning process be optimized to minimize the number of human annotations required, and what are the trade-offs between annotation cost and model accuracy?
- What are some real-world applications of combining active learning and human-in-the-loop methods, such as in medical diagnosis or autonomous driving, and what are the benefits of this approach?
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