ConBatch-BAL: Batch Bayesian Active Learning under Budget Constraints
This addresses the challenge of costly data annotation and budget limitations in active learning for real-world applications like geospatial analysis, but it is incremental as it builds on existing Bayesian active learning methods.
This work tackles the problem of batch Bayesian active learning under budget constraints by introducing two strategies, dynamic thresholding and greedy acquisition, which use Bayesian neural networks to select samples. The results show that these strategies reduce active learning iterations and data acquisition costs, outperforming unconstrained baselines on new real-world datasets of geolocated aerial images.
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.