  Supervised deep learning methods are enjoying enormous success in many
practical applications of computer vision and have the potential to
revolutionize robotics. However, the marked performance degradation to biases
and imbalanced data questions the reliability of these methods. In this work we
address these questions from the perspective of dataset imbalance resulting out
of severe under-representation of annotated training data for certain classes
and its effect on both deep classification and generation methods. We introduce
a joint dataset repairment strategy by combining a neural network classifier
with Generative Adversarial Networks (GAN) that makes up for the deficit of
training examples from the under-representated class by producing additional
training examples. We show that the combined training helps to improve the
robustness of both the classifier and the GAN against severe class imbalance.
We show the effectiveness of our proposed approach on three very different
datasets with different degrees of imbalance in them. The code is available at
https://github.com/AadSah/ImbalanceCycleGAN .
