Greedy auto-augmentation for n-shot learning using deep neural networks

The goal of n-shot learning is the classification of input data from small datasets. This type of learning is challenging in neural networks, which typically need a high number of data during the training process. Recent advancements in data augmentation allow us to produce an infinite number of target conditions from the primary condition. This process includes two main steps for finding the best augmentations and training the data with the new augmentation techniques. Optimizing these two steps for n-shot learning is still an open problem. In this paper, we propose a new method for auto-augmentation to address both of these problems. The proposed method can potentially extract many possible types of information from a small number of available data points in n-shot learning. The results of our experiments on five prominent n-shot learning datasets show the effectiveness of the proposed method.