  Distilling knowledge from huge pre-trained networks to improve the
performance of tiny networks has favored deep learning models to be used in
many real-time and mobile applications. Several approaches that demonstrate
success in this field have made use of the true training dataset to extract
relevant knowledge. In absence of the True dataset, however, extracting
knowledge from deep networks is still a challenge. Recent works on data-free
knowledge distillation demonstrate such techniques on classification tasks. To
this end, we explore the task of data-free knowledge distillation for
segmentation tasks. First, we identify several challenges specific to
segmentation. We make use of the DeGAN training framework to propose a novel
loss function for enforcing diversity in a setting where a few classes are
underrepresented. Further, we explore a new training framework for performing
knowledge distillation in a data-free setting. We get an improvement of 6.93%
in Mean IoU over previous approaches.
