  Active learning emerged as an alternative to alleviate the effort to label
huge amount of data for data hungry applications (such as image/video indexing
and retrieval, autonomous driving, etc.). The goal of active learning is to
automatically select a number of unlabeled samples for annotation (according to
a budget), based on an acquisition function, which indicates how valuable a
sample is for training the model. The learning loss method is a task-agnostic
approach which attaches a module to learn to predict the target loss of
unlabeled data, and select data with the highest loss for labeling. In this
work, we follow this strategy but we define the acquisition function as a
learning to rank problem and rethink the structure of the loss prediction
module, using a simple but effective listwise approach. Experimental results on
four datasets demonstrate that our method outperforms recent state-of-the-art
active learning approaches for both image classification and regression tasks.
