Weighted Ensembles for Active Learning with Adaptivity
This work addresses efficient model training in domains like medical imaging and robotics, but it is incremental as it builds on existing GP-based active learning methods.
The paper tackled the problem of high labeling costs in active learning by proposing an ensemble of Gaussian process models with adaptive weights, which outperformed single GP-based alternatives on synthetic and real datasets.
Labeled data can be expensive to acquire in several application domains, including medical imaging, robotics, and computer vision. To efficiently train machine learning models under such high labeling costs, active learning (AL) judiciously selects the most informative data instances to label on-the-fly. This active sampling process can benefit from a statistical function model, that is typically captured by a Gaussian process (GP). While most GP-based AL approaches rely on a single kernel function, the present contribution advocates an ensemble of GP models with weights adapted to the labeled data collected incrementally. Building on this novel EGP model, a suite of acquisition functions emerges based on the uncertainty and disagreement rules. An adaptively weighted ensemble of EGP-based acquisition functions is also introduced to further robustify performance. Extensive tests on synthetic and real datasets showcase the merits of the proposed EGP-based approaches with respect to the single GP-based AL alternatives.