  Despite the recent developments in vision-related problems using deep neural
networks, there still remains a wide scope in the improvement of generalizing
these models to unseen examples. In this paper, we explore the domain of
few-shot learning with a novel augmentation technique. In contrast to other
generative augmentation techniques, where the distribution over input images
are learnt, we propose to learn the probability distribution over the image
transformation parameters which are easier and quicker to learn. Our technique
is fully differentiable which enables its extension to versatile data-sets and
base models. We evaluate our proposed method on multiple base-networks and 2
data-sets to establish the robustness and efficiency of this method. We obtain
an improvement of nearly 4% by adding our augmentation module without making
any change in network architectures. We also make the code readily available
for usage by the community.
