Learning Based on Generative AI With Image Synthesis and Data Augmentation Techniques
Alok Jain, Dharmendra Kumar Roy, Firas Tayseer Ayasrah, P. William, G. Prasanna Lakshmi
Abstract
Generative artificial intelligence enables improved image synthesis and data augmentation in computer vision. GANs and VAEs are essential. VAEs learn using data distributions, whereas GANs employ generator-discriminator architecture to generate realistic pictures. These technologies have improved data augmentation and produced a wide diversity of synthetic data. This study uses GANs to create a visual creative generative AI model. The study process includes data collection, model creation, participant training, and final evaluation. Generative artificial intelligence improves statistical model performance and durability during data augmentation. This study examines the scientific and ethical implications of this technological accomplishment. Generative artificial intelligence's effects on several fields are also examined.