학술논문

Comparative Analysis of Generative AI Models
Document Type
Conference
Source
2023 International Conference on Advances in Computation, Communication and Information Technology (ICAICCIT) Advances in Computation, Communication and Information Technology (ICAICCIT), 2023 International Conference on. :760-765 Nov, 2023
Subject
Bioengineering
Communication, Networking and Broadcast Technologies
Computing and Processing
Engineering Profession
General Topics for Engineers
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Analytical models
Adaptation models
Generative AI
Image synthesis
Transfer learning
Training data
Transformers
GPT
ChatGPT
OpenAI
Generative Adversarial Networks (GANs)
Language
Abstract
Generative AI models have the potential to create a variety of new content based on training data. They are not only able to create textual content but also other multimedia content such as images, audio, video, etc. They have gained popularity in recent years as they have a major impact on various fields. They are used for several applications from text generation, image generation, and music composition to education, healthcare, and metaverse. Still, several challenges are faced while developing and applying these models i.e., trustworthiness, biased content, overfitting and regulatory concerns. In this paper, the comparative analysis of various generative AI models concerning different parameters is performed with respect to tools, frameworks, input, output, development authority, etc. In addition to these, Applications of different generative AI Models are discussed in various domains