Generative Adversarial Networks (GANs) and Diffusion-based Text-to-Image Synthesis Framework for Enhancing English Vocabulary Learning in Early Childhood Education
Keywords:
Chatbots, Diffusion models, Generative adversarial networks, Large language models, Text-to-imageAbstract
This study presents the development of a generative adversarial network (GAN) based text-to-image synthesizer utilizing both adversarial and diffusion techniques. In fact, in order to transform natural language into visually meaningful images, this system uses the power of GANs and a diffusion model in combination to generate diverse and high-quality images. Moreover, a large language model (LLM) based chatbot is integrated into the system, allowing users to interactively work on the image generation process. A web-based application allows users to interact with the system, aiding them in developing conversations with the chatbot, and to receive generated images. This project overcomes the limitations of existing text-to-image generation methods by combining the strengths of GANs, diffusion models, and LLMs. The study also describes the underlying methodology of the complete project, covering the text-to-image model, chatbot integration, and web application. Finally, the results are presented with a discussion of potential future research directions.
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