A Framework and Benchmark for Deep Batch Active Learning for Regression. (arXiv:2203.09410v2 [stat.ML] UPDATED)

The acquisition of labels for supervised learning can be expensive. In order
to improve the sample-efficiency of neural network regression, we study active
learning methods that adaptively select batches of unlabeled data for labeling.
We present a framework for constructing such methods out of (network-dependent)
base kernels, kernel transformations and selection methods. Our framework
encompasses many existing Bayesian methods based on Gaussian Process
approximations of neural networks as well as non-Bayesian methods.
Additionally, we propose to replace the commonly used …

active learning arxiv benchmark framework learning ml regression