Leveraging Generative AI to Enhance CIFAR-100 Classification

Generative Artificial Intelligence has revolutionized the way we generate data, thanks to breakthrough technologies like GANs. By employing a multi-model evaluation approach, this research aims to identify a model that seamlessly integrates synthesis information, thereby enhancing accuracy and generalization in image classification systems. Leveraging GANs in conjunction with popular models such as VGG, ResNet, and ImageNet, this study promotes mutual reinforcement to improve picture classification performance. The methodology involves a systematic approach to address research questions and achieve study goals, emphasizing transparency, and reproducibility in the research process. By delving into the intricacies of CIFAR-100, the research sets out to assess the effectiveness of generative models in efficiently generating a range of visual content. These findings provide contributions to the continual advancement of cutting-edge image synthesis methods. This paper explores analysis of synthetic images through the integration of pre-trained models, focusing on feature representation, abstraction, and class discrimination.