Few-shot learning for defence and security

Abstract

In Defence and Security, we are often interested in rare events and occurrences where we only have a few examples. This presents a problem for traditional machine learning approaches which typically require thousands of examples per class to learn an effective classifier. Few-shot learning techniques look to use tasks comprising of a small set of labelled images and a novel, unlabelled example, to classify the novel example. These are typically considered as N-way k-shot problems, where we have N distinct classes, with only k labelled examples per class. At the Defence Science and Technology Laboratory (Dstl) in the UK, we are looking to understand the application of few-shot learning techniques to Defence and Security problems, particularly on imagery datasets. In this paper we discuss the application of few-shot learning approaches from literature to Defence and Security problems and discuss meta-learning, one of the key types of approaches to few-shot learning. We also present experimentation on meta-learning models, baselined against a transfer learned ResNet, across a range of few-shot tasks with differing data proportions on the miniImageNet and Caltech-USCD Birds datasets. This experimentation looks to improve our understanding of the behaviour of these few-shot models on a range of data limited problems. We identify a number of challenges that require further research before few-shot approaches can be effectively applied to Defence and Security problems and further research that could increase the range of Defence and Security problems to which few-shot approaches could be applied.