Deep learning and its application to general image classification

Abstract

Deep learning has recently exhibited good performance in many applications. The convolution neural network is an often-used architecture for deep learning and has been widely used in computer vision and audio recognition, and outperformed other related handcraft designed feature in recent years. These techniques compared to other artificial intelligence algorithms and handcraft features need extremely much more time in training and testing and then were not widely used in the early days. Our study is about the impacts of different factors used in the convolution neural network. The considered factors are network depth, numbers of filters, and filter sizes. The used data set is the CIFAR dataset. According to our experiments, some suggestions about those factors are recommended in this study.