Comparative Study of Deep Learning Neural Networks for Image Classification

Abstract

Image classification plays a crucial role in image recognition in present times. Researchers have developed numerous methods for achieving image classification through long-term research. Deep Learning, as one of the most important methodologies, is used to clarify the contents of an image. This paper provides an overview of four typical deep learning neural networks used for image classification, including conventional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), and deep neural networks (DNN). The architecture, function, and applications of each network are discussed and compared. CNN is commonly used for image recognition, while RNN is suitable for speech recognition, natural language processing, and time series forecasting. DNN is ideal for handling high-dimensional data, and GAN can generate new data samples. The StyleGAN is introduced as an application of GAN, which can produce high-quality images. Finally, the future work and challenges in image recognition are discussed.