Improving Image Classification using Triple-GAN: A Fusion of Generative Adversarial Networks and Transfer Learning
Danial Ebrahimzadeh, Safura Sharifi, Yaser Mike Banad · 2023
Generative neural networks, particularly Generative Adversarial Networks (GANs), have gained a significant attention for their ability to generate new data. Moreover, transfer learning, known for its efficient processing and promising research outcomes, has emerged as a popular approach for feature extraction. This paper explores the application of the Triple-GAN model, which combines the advantages of GANs with transfer learning, particularly in the context of image classification. The study focuses on utilizing the Triple-GAN model combined with a pre-trained ResNet101 model for image classification. The evaluation of the proposed approach is conducted on the CIFAR-100 dataset, employing specific metrics to assess the model’s efficacy. The results highlight the promising nature of the proposed model, showcasing its potential for accurate image classification. Our model exhibits a remarkable level of accuracy reaching above 71.52 % outperforming other conventional ANN models. The significance of this research lies in its potential to advance image classification techniques by employing the Triple-GAN model with transfer learning as a viable alternative to traditional classification methods.