A comparative study of generative adversarial networks for image recognition algorithms based on deep learning and traditional methods

TL;DR

This study compares traditional image recognition methods with GAN-based deep learning approaches, demonstrating GAN's superior performance in complex image recognition, noise handling, and detail capture through extensive experiments.

Contribution

The paper provides a comprehensive comparison of traditional methods and GAN-based deep learning for image recognition, highlighting GAN's advantages and application potential.

Findings

GAN outperforms traditional methods in recognition accuracy

GAN exhibits better noise robustness and detail preservation

Experimental results validate GAN's effectiveness on multiple datasets

Abstract

In this paper, an image recognition algorithm based on the combination of deep learning and generative adversarial network (GAN) is studied, and compared with traditional image recognition methods. The purpose of this study is to evaluate the advantages and application prospects of deep learning technology, especially GAN, in the field of image recognition. Firstly, this paper reviews the basic principles and techniques of traditional image recognition methods, including the classical algorithms based on feature extraction such as SIFT, HOG and their combination with support vector machine (SVM), random forest, and other classifiers. Then, the working principle, network structure, and unique advantages of GAN in image generation and recognition are introduced. In order to verify the effectiveness of GAN in image recognition, a series of experiments are designed and carried out using multiple public image data sets for training and testing. The experimental results show that compared with traditional methods, GAN has excellent performance in processing complex images, recognition accuracy, and anti-noise ability. Specifically, Gans are better able to capture high-dimensional features and details of images, significantly improving recognition performance. In addition, Gans shows unique advantages in dealing with image noise, partial missing information, and generating high-quality images.