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ConBatch-BAL: Batch Bayesian Active Learning under Budget Constraints

arXiv 2507.04929 ↗First posted 7 Jul 2025cs.LG cs.CVcited by 0

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Abstract (arXiv)

Varying annotation costs among data points and budget constraints can hinder the adoption of active learning strategies in real-world applications. This work introduces two Bayesian active learning strategies for batch acquisition under constraints (ConBatch-BAL), one based on dynamic thresholding and one following greedy acquisition. Both select samples using uncertainty metrics computed via Bayesian neural networks. The dynamic thresholding strategy redistributes the budget across the batch, while the greedy one selects the top-ranked sample at each step, limited by the remaining budget. Focusing on scenarios with costly data annotation and geospatial constraints, we also release two new real-world datasets containing geolocated aerial images of buildings, annotated with energy efficiency or typology classes. The ConBatch-BAL strategies are benchmarked against a random acquisition baseline on these datasets under various budget and cost scenarios. The results show that the developed ConBatch-BAL strategies can reduce active learning iterations and data acquisition costs in real-world settings, and even outperform the unconstrained baseline solutions.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 25 of its 42 references that are in the map. First posted on arXiv 7 Jul 2025.

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25 of 42 references.

References · 25

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13 more
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