MA 3 : Model Agnostic Adversarial Augmentation for Few Shot learning
This addresses the problem of generalization to unseen examples in few-shot learning for vision tasks, representing an incremental advancement.
The paper tackles few-shot learning by proposing a novel augmentation technique that learns probability distributions over image transformation parameters rather than input images, achieving nearly 4% improvement without changing network architectures.
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.