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Related Questions
- How do you select the most informative samples for active learning in computer vision, and what are the key factors to consider during the selection process?
- Can you explain the concept of uncertainty sampling and its application in active learning for computer vision tasks, and how it can help reduce the need for large datasets?
- What are the different active learning strategies that can be employed in computer vision, such as uncertainty-based, query-by-committee, and expected model change, and how do they work?
- How do you deal with the issue of class imbalance in active learning, and what techniques can be used to handle it in computer vision tasks?
- Can you discuss the role of diversity in active learning, and how it can be achieved in computer vision to ensure that the model learns from a diverse set of samples?
- What are the metrics used to evaluate the performance of active learning in computer vision, and how can they be used to determine the optimal number of samples to select for active learning?
- How can active learning be applied to real-world computer vision tasks, such as object detection, image classification, and segmentation, and what are the benefits and challenges of using active learning in these tasks?
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