  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 in a Single-Training Multi-Inference
fashion. 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.
Despite its simplicity, our method outperforms the state-of-the-art active
learning baselines in various tasks, including computer vision, natural
language processing, and structural data analysis.

Machine Learning