Work

Text-to-Image Generation Using Stack Generative Adversarial Networks (GANs) and Stable Diffusion Models

Year: 2024

Type: article

Abstract: Text-to-image synthesis is a challenging task in artificial intelligence that involves generating realistic images from textual descriptions. StackGAN, [1] a generative model with a two-stage architecture, has proven to be effective in improving the quality and fidelity of synthesized images. In this study, we implement and evaluate the performance of StackGAN using the CIFAR-10 dataset, demonstrating how a multi-stage generation approach can produce high-quality images from textual labels. The experimental results show that StackGAN successfully captures the underlying features of the dataset categories and provides reasonable image fidelity.

Source: International Journal for Research in Applied Science and Engineering Technology

DOI: 10.22214/ijraset.2024.65350

Author: Shuhaab Shafi

Institution: MIT World Peace University

Language: English

Topic: Generative Adversarial Networks and Image Synthesis

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

OpenAlex ID: W4404650695

FWCI: 0.288

Cites:

Cited by: 2

Related to: 10