A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPTA Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT - A Comprehensive Survey of AI-Generated Content (AIGC) - A History of Generative AI from GAN to ChatGPT.pdf

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• individuals have become interested in related resources and are seeking to uncover the background and secrets behind its impressive performance. In fact, ChatGPT and other Generative AI (GAI) techniques belong to the category of Artificial Intelligence Generated Content (AIGC), which involves the creation of digital content, such as images, music, and natural language, through AI models. The goal of AIGC is to make the content creation process more efficient and accessible, allowing for the production of high-quality content at a faster

• the model can learn becomes more comprehensive and closer to reality, leading to more realistic and high-quality content generation. This survey provides a comprehensive review on the history of generative models, and basic components, recent advances in AIGC from unimodal interaction and multimodal interaction. From the perspective of unimodality, we introduce the generation tasks and relative models of text and image. From the pers

• N, Lehigh University, USA YUTONG DAI, Lehigh University, USA PHILIP S. YU, Uni

• e images. In 2014, Generative Adversarial Networks (GANs) [ 29] was first proposed, which was a significant milestone in this area, due to its impressive results in various applications. Variational Autoencoders (VAEs) [30] and other methods like diffusion generative models [31] have also been developed for more fine-grained control over the image generation process and the ability to generate high