Two budget-aware batch active learning heuristics, greedy and dynamic thresholding, reduce the number of labeling rounds needed to reach target accuracy on new building image datasets compared with random selection.
Optimised probabilistic active learning (OPAL) for fast, non-myopic, cost-sensitive active classification
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ConBatch-BAL: Batch Bayesian Active Learning under Budget Constraints
Two budget-aware batch active learning heuristics, greedy and dynamic thresholding, reduce the number of labeling rounds needed to reach target accuracy on new building image datasets compared with random selection.