Training a Deep Learning Network with an Insignificantly Small Dataset

Abstract

In the last few years, Deep Learning is one of the top research areas in academia as well as in industry. Every industry is now looking for a deep learning-based solution to the problems in hand. As a researcher, learning “Deep Learning” through practical experiments will be a very challenging task. Particularly, training a deep learning network with huge amount of training data will make it impractical to do this on a normal desktop computer or laptop. Even a small-scale application in computer vision using deep learning techniques will require several days of training the deep network model on a very higher end Graphical Processing Unit (GPU) clusters or Tensor Processing Unit (TPU) clusters that makes impractical to do that research on a conventional laptop. In this work, we address the possibilities of training a deep learning network with an insignificantly small dataset. Here we mean “significantly small dataset’ as a dataset with only few images (<10) per class. Since we are going to design a prototype drone detection system which is a single class classification problem, we hereby try to train the deep learning network only with few drone images (2 images only). Our research question is: will it be possible to train a YOLO deep learning network model only with two images and achieve a descent detection accurate on a constrained test dataset of drones? This paper addresses that issue and our results prove that it is possible to train a deep learning network only with two images and achieve good performance under constrained application environments.