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

arXiv: https://arxiv.org/abs/2408.03568 · HTML: https://mlsanity.com/paper/2408.03568/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 28 references that are in the map.
- First posted on arXiv 7 Aug 2024.

## Builds on (most cited first, 15 of 15)

- [Survival Prediction Across Diverse Cancer Types Using Neural Networks](https://mlsanity.com/paper/2404.08713/) (2024)
- [Convolutional neural network classification of cancer cytopathology images: taking breast cancer as an example](https://mlsanity.com/paper/2404.08279/) (2024)
- [Generative Text-Guided 3D Vision-Language Pretraining for Unified Medical Image Segmentation](https://mlsanity.com/paper/2306.04811/) (2023)
- [Improving Depth Gradient Continuity in Transformers: A Comparative Study on Monocular Depth Estimation with CNN](https://mlsanity.com/paper/2308.08333/) (2023)
- [Efficiency optimization of large-scale language models based on deep learning in natural language processing tasks](https://mlsanity.com/paper/2405.11704/) (2024)
- [Exploration of Attention Mechanism-Enhanced Deep Learning Models in the Mining of Medical Textual Data](https://mlsanity.com/paper/2406.00016/) (2024)
- [An Enhanced Encoder-Decoder Network Architecture for Reducing Information Loss in Image Semantic Segmentation](https://mlsanity.com/paper/2406.01605/) (2024)
- [DF-DM: A foundational process model for multimodal data fusion in the artificial intelligence era](https://mlsanity.com/paper/2404.12278/) (2024)
- [Research on Intelligent Aided Diagnosis System of Medical Image Based on Computer Deep Learning](https://mlsanity.com/paper/2404.18419/) (2024)
- [Integrating Medical Imaging and Clinical Reports Using Multimodal Deep Learning for Advanced Disease Analysis](https://mlsanity.com/paper/2405.17459/) (2024)
- [Research on Deep Learning Model of Feature Extraction Based on Convolutional Neural Network](https://mlsanity.com/paper/2406.08837/) (2024)
- [Research on Early Warning Model of Cardiovascular Disease Based on Computer Deep Learning](https://mlsanity.com/paper/2406.08864/) (2024)
- [Investigation of Customized Medical Decision Algorithms Utilizing Graph Neural Networks](https://mlsanity.com/paper/2405.17460/) (2024)
- [Advancing Financial Risk Prediction Through Optimized LSTM Model Performance and Comparative Analysis](https://mlsanity.com/paper/2405.20603/) (2024)
- [Research on Optimization of Natural Language Processing Model Based on Multimodal Deep Learning](https://mlsanity.com/paper/2406.08838/) (2024)

## Built on by (most cited first, 12 of 12)

- [A Lightweight GAN-Based Image Fusion Algorithm for Visible and Infrared Images](https://mlsanity.com/paper/2409.15332/) (2024)
- [Wasserstein Distance-Weighted Adversarial Network for Cross-Domain Credit Risk Assessment](https://mlsanity.com/paper/2409.18544/) (2024)
- [Unveiling the Potential of Graph Neural Networks in SME Credit Risk Assessment](https://mlsanity.com/paper/2409.17909/) (2024)
- [Contrastive Learning for Knowledge-Based Question Generation in Large Language Models](https://mlsanity.com/paper/2409.13994/) (2024)
- [Applying Hybrid Graph Neural Networks to Strengthen Credit Risk Analysis](https://mlsanity.com/paper/2410.04283/) (2024)
- [Enhancing Deep Learning with Optimized Gradient Descent: Bridging Numerical Methods and Neural Network Training](https://mlsanity.com/paper/2409.04707/) (2024)
- [Reducing Bias in Deep Learning Optimization: The RSGDM Approach](https://mlsanity.com/paper/2409.15314/) (2024)
- [Optimizing News Text Classification with Bi-LSTM and Attention Mechanism for Efficient Data Processing](https://mlsanity.com/paper/2409.15576/) (2024)
- [Deep Learning-Based Channel Squeeze U-Structure for Lung Nodule Detection and Segmentation](https://mlsanity.com/paper/2409.13868/) (2024)
- [Graph Neural Network Framework for Sentiment Analysis Using Syntactic Feature](https://mlsanity.com/paper/2409.14000/) (2024)
- [Stock Type Prediction Model Based on Hierarchical Graph Neural Network](https://mlsanity.com/paper/2412.06862/) (2024)
- [C-DIRA: Computationally Efficient Dynamic ROI Routing and Domain-Invariant Adversarial Learning for Lightweight Driver Behavior Recognition](https://mlsanity.com/paper/2512.08647/) (2025)

## Abstract (arXiv)

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.