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Related Questions
- What specific scenarios in active learning leverage uncertainty-based sampling to mitigate labeling needs?
- How does uncertainty-based sampling prioritize model uncertainty to augment the value of labeled examples?
- Can uncertainty-based sampling be used for both low-data and high-data domains to reduce labeling requirements?
- Are there different types of uncertainty (aleatoric and epistemic) addressed by uncertainty-based sampling for active learning?
- How does the choice of uncertainty metrics (e.g., entropy, variance, or Monte Carlo dropout) impact the effectiveness of active learning with uncertainty-based sampling?
- Can active learning with uncertainty-based sampling improve model generalization by sampling from uncertain regions of the input space?
- What are the potential downsides of relying on uncertainty-based sampling, such as over-reliance on a particular uncertainty metric?
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