  We propose Disentanglement based Active Learning (DAL), a new active learning
technique based on self-supervision which leverages the concept of
disentanglement. Instead of requesting labels from human oracle, our method
automatically labels the majority of the datapoints, thus drastically reducing
the human labeling budget in Generative Adversarial Net (GAN) based active
learning approaches. The proposed method uses Information Maximizing Generative
Adversarial Nets (InfoGAN) to learn disentangled class category
representations. Disagreement between active learner predictions and InfoGAN
labels decides if the datapoints need to be human-labeled. We also introduce a
label correction mechanism that aims to filter out label noise that occurs due
to automatic labeling. Results on three benchmark datasets for the image
classification task demonstrate that our method achieves better performance
compared to existing GAN-based active learning approaches.
