  Uncertainty estimation for unlabeled data is crucial to active learning. With
a deep neural network employed as the backbone model, the data selection
process is highly challenging due to the potential over-confidence of the model
inference. Existing methods resort to special learning fashions (e.g.
adversarial) or auxiliary models to address this challenge. This tends to
result in complex and inefficient pipelines, which would render the methods
impractical. In this work, we propose a novel algorithm that leverages noise
stability to estimate data uncertainty. The key idea is to measure the output
derivation from the original observation when the model parameters are randomly
perturbed by noise. We provide theoretical analyses by leveraging the small
Gaussian noise theory and demonstrate that our method favors a subset with
large and diverse gradients. Our method is generally applicable in various
tasks, including computer vision, natural language processing, and structural
data analysis. It achieves competitive performance compared against
state-of-the-art active learning baselines.
