mlsanityData to 7 Oct 2026

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

arXiv 2408.03568 ↗First posted 7 Aug 2024cs.CV cs.LG eess.IVcited by 12

Not on the map. Why?

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.

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

Builds on

15 of 28 references.

References · 15

  1. 2024Survival Prediction Across Diverse Cancer Types Using Neural Networkscited by 80
  2. 2024Convolutional neural network classification of cancer cytopathology images: taking breast cancer as an examplecited by 50
  3. 2023Generative Text-Guided 3D Vision-Language Pretraining for Unified Medical Image Segmentationcited by 47
  4. 2023Improving Depth Gradient Continuity in Transformers: A Comparative Study on Monocular Depth Estimation with CNNcited by 35
  5. 2024Efficiency optimization of large-scale language models based on deep learning in natural language processing taskscited by 14
  6. 2024Exploration of Attention Mechanism-Enhanced Deep Learning Models in the Mining of Medical Textual Datacited by 11
  7. 2024An Enhanced Encoder-Decoder Network Architecture for Reducing Information Loss in Image Semantic Segmentationcited by 10
  8. 2024DF-DM: A foundational process model for multimodal data fusion in the artificial intelligence eracited by 9
  9. 2024Research on Intelligent Aided Diagnosis System of Medical Image Based on Computer Deep Learningcited by 8
  10. 2024Integrating Medical Imaging and Clinical Reports Using Multimodal Deep Learning for Advanced Disease Analysiscited by 8
  11. 2024Research on Deep Learning Model of Feature Extraction Based on Convolutional Neural Networkcited by 7
  12. 2024Research on Early Warning Model of Cardiovascular Disease Based on Computer Deep Learningcited by 6
3 more
  1. 2024Investigation of Customized Medical Decision Algorithms Utilizing Graph Neural Networkscited by 5
  2. 2024Advancing Financial Risk Prediction Through Optimized LSTM Model Performance and Comparative Analysiscited by 5
  3. 2024Research on Optimization of Natural Language Processing Model Based on Multimodal Deep Learningcited by 5

Built on by

Cited by year

Citing papers per year: 2024: 11; 2025: 1.

12 papers.

  1. 2024A Lightweight GAN-Based Image Fusion Algorithm for Visible and Infrared Imagescited by 11
  2. 2024Wasserstein Distance-Weighted Adversarial Network for Cross-Domain Credit Risk Assessmentcited by 11
  3. 2024Unveiling the Potential of Graph Neural Networks in SME Credit Risk Assessmentcited by 9
  4. 2024Contrastive Learning for Knowledge-Based Question Generation in Large Language Modelscited by 8
  5. 2024Applying Hybrid Graph Neural Networks to Strengthen Credit Risk Analysiscited by 8
  6. 2024Enhancing Deep Learning with Optimized Gradient Descent: Bridging Numerical Methods and Neural Network Trainingcited by 7
  7. 2024Reducing Bias in Deep Learning Optimization: The RSGDM Approachcited by 7
  8. 2024Optimizing News Text Classification with Bi-LSTM and Attention Mechanism for Efficient Data Processingcited by 7
  9. 2024Deep Learning-Based Channel Squeeze U-Structure for Lung Nodule Detection and Segmentationcited by 6
  10. 2024Graph Neural Network Framework for Sentiment Analysis Using Syntactic Featurecited by 6
  11. 2024Stock Type Prediction Model Based on Hierarchical Graph Neural Networkcited by 5
  12. 2025C-DIRA: Computationally Efficient Dynamic ROI Routing and Domain-Invariant Adversarial Learning for Lightweight Driver Behavior Recognitioncited by 0