Computationally efficient ANN model for small-scale problems

Abstract

In this current age of digital photography, the digital information is expanding exponentially. The use of such information in fields like research, automation, etc. has experienced a rise over the last decade. Also, employing machines to automate any task has been performed since forever. This leads to extensive use of the machine in solving the task of understanding the digital information called computer vision. Machine learning has always played an eminent role in various computer vision challenges. But, with the emergence of deep learning, machines are now outperforming humans. This has led to exaggerate the use of such deep learning techniques like convolutional neural network (CNN) in almost every machine vision task. In this paper, a new technique is proposed that could be used in place of CNN for solving elementary computer vision problems. The work uses the ability of the spatial transformer networks (STN) to effectively extract the spatial information from an input. Such an information is invariant and could be used as input to more plain neural networks like artificial neural network (ANN) without performance being compromised.