Improving Techniques for Convolutional Neural Networks Performance

Convolutional Neural Networks (CNNs) have been extensively used in several application domains. Researchers have been exploring methods to enhance the accuracy of applications in accuracy-critical domains by either increasing the depth or width of the network. The presence of structures results in a significant increase in both computational and storage costs, hence causing a delay in response time. Convolutional Neural Networks have significantly contributed to the rapid development of several applications, including image classification, object detection, and semantic segmentation. However, in some applications that need zero tolerance for mistakes, such as automated systems, there are still certain issues that need to be addressed to achieve better performance. Then, despite the progress made so far, there are still limitations and challenges that must be overcome. Simultaneously, there is a need for reduced reaction time. Convolutional Neural Networks (CNNs) are now faced with significant obstacles of a formidable nature. This paper investigates different methods that can be used to improve convolutional neural network performance.