  Recent state-of-the-art active learning methods have mostly leveraged
Generative Adversarial Networks (GAN) for sample acquisition; however, GAN is
usually known to suffer from instability and sensitivity to hyper-parameters.
In contrast to these methods, we propose in this paper a novel active learning
framework that we call Maximum Classifier Discrepancy for Active Learning
(MCDAL) which takes the prediction discrepancies between multiple classifiers.
In particular, we utilize two auxiliary classification layers that learn
tighter decision boundaries by maximizing the discrepancies among them.
Intuitively, the discrepancies in the auxiliary classification layers'
predictions indicate the uncertainty in the prediction. In this regard, we
propose a novel method to leverage the classifier discrepancies for the
acquisition function for active learning. We also provide an interpretation of
our idea in relation to existing GAN based active learning methods and domain
adaptation frameworks. Moreover, we empirically demonstrate the utility of our
approach where the performance of our approach exceeds the state-of-the-art
methods on several image classification and semantic segmentation datasets in
active learning setups.
