  Active learning(AL) has recently gained popularity for deep learning(DL)
models. This is due to efficient and informative sampling, especially when the
learner requires large-scale labelled datasets. Commonly, the sampling and
training happen in stages while more batches are added. One main bottleneck in
this strategy is the narrow representation learned by the model that affects
the overall AL selection.
  We present MoBYv2AL, a novel self-supervised active learning framework for
image classification. Our contribution lies in lifting MoBY, one of the most
successful self-supervised learning algorithms, to the AL pipeline. Thus, we
add the downstream task-aware objective function and optimize it jointly with
contrastive loss. Further, we derive a data-distribution selection function
from labelling the new examples. Finally, we test and study our pipeline
robustness and performance for image classification tasks. We successfully
achieved state-of-the-art results when compared to recent AL methods. Code
available: https://github.com/razvancaramalau/MoBYv2AL
