  Conventional active learning (AL) frameworks aim to reduce the cost of data
annotation by actively requesting the labeling for the most informative data
points. However, introducing AL to data hungry deep learning algorithms has
been a challenge. Some proposed approaches include uncertainty-based
techniques, geometric methods, implicit combination of uncertainty-based and
geometric approaches, and more recently, frameworks based on semi/self
supervised techniques. In this paper, we address two specific problems in this
area. The first is the need for efficient exploitation/exploration trade-off in
sample selection in AL. For this, we present an innovative integration of
recent progress in both uncertainty-based and geometric frameworks to enable an
efficient exploration/exploitation trade-off in sample selection strategy. To
this end, we build on a computationally efficient approximate of Thompson
sampling with key changes as a posterior estimator for uncertainty
representation. Our framework provides two advantages: (1) accurate posterior
estimation, and (2) tune-able trade-off between computational overhead and
higher accuracy. The second problem is the need for improved training protocols
in deep AL. For this, we use ideas from semi/self supervised learning to
propose a general approach that is independent of the specific AL technique
being used. Taken these together, our framework shows a significant improvement
over the state-of-the-art, with results that are comparable to the performance
of supervised-learning under the same setting. We show empirical results of our
framework, and comparative performance with the state-of-the-art on four
datasets, namely, MNIST, CIFAR10, CIFAR100 and ImageNet to establish a new
baseline in two different settings.
