  While deep learning succeeds in a wide range of tasks, it highly depends on
the massive collection of annotated data which is expensive and time-consuming.
To lower the cost of data annotation, active learning has been proposed to
interactively query an oracle to annotate a small proportion of informative
samples in an unlabeled dataset. Inspired by the fact that the samples with
higher loss are usually more informative to the model than the samples with
lower loss, in this paper we present a novel deep active learning approach that
queries the oracle for data annotation when the unlabeled sample is believed to
incorporate high loss. The core of our approach is a measurement Temporal
Output Discrepancy (TOD) that estimates the sample loss by evaluating the
discrepancy of outputs given by models at different optimization steps. Our
theoretical investigation shows that TOD lower-bounds the accumulated sample
loss thus it can be used to select informative unlabeled samples. On basis of
TOD, we further develop an effective unlabeled data sampling strategy as well
as an unsupervised learning criterion that enhances model performance by
incorporating the unlabeled data. Due to the simplicity of TOD, our active
learning approach is efficient, flexible, and task-agnostic. Extensive
experimental results demonstrate that our approach achieves superior
performances than the state-of-the-art active learning methods on image
classification and semantic segmentation tasks.
