  In addition to high accuracy, robustness is becoming increasingly important
for machine learning models in various applications. Recently, much research
has been devoted to improving the model robustness by training with noise
perturbations. Most existing studies assume a fixed perturbation level for all
training examples, which however hardly holds in real tasks. In fact, excessive
perturbations may destroy the discriminative content of an example, while
deficient perturbations may fail to provide helpful information for improving
the robustness. Motivated by this observation, we propose to adaptively adjust
the perturbation levels for each example in the training process. Specifically,
a novel active learning framework is proposed to allow the model to
interactively query the correct perturbation level from human experts. By
designing a cost-effective sampling strategy along with a new query type, the
robustness can be significantly improved with a few queries. Both theoretical
analysis and experimental studies validate the effectiveness of the proposed
approach.
