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A Comprehensive Survey of AI-Generated Content (AIGC):
A History of Generative AI from GAN to ChatGPT
YIHAN CAO
∗
, Lehigh University & Carnegie Mellon University, USA
SIYU LI, Lehigh University, USA
YIXIN LIU, Lehigh University, USA
ZHILING YAN, Lehigh University, USA
YUTONG DAI, Lehigh University, USA
PHILIP S. YU, University of Illinois at Chicago, USA
LICHAO SUN, Lehigh University, USA
Recently, ChatGPT, along with DALL-E-2 [
1
] and Codex [
2
],has been gaining signicant attention from society.
As a result, many 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 Articial 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 ecient and accessible, allowing for the production of
high-quality content at a faster pace. AIGC is achieved by extracting and understanding intent information
from instructions provided by human, and generating the content according to its knowledge and the intent
information. In recent years, large-scale models have become increasingly important in AIGC as they provide
better intent extraction and thus, improved generation results. With the growth of data and the size of the
models, the distribution that 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 perspective of multimodality, we introduce the cross-application between
the modalities mentioned above. Finally, we discuss the existing open problems and future challenges in AIGC.
CCS Concepts:
• Computer systems organization → Embedded systems
; Redundancy; Robotics;
• Net-
works → Network reliability.
Additional Key Words and Phrases: datasets, neural networks, gaze detection, text tagging
ACM Reference Format:
Yihan Cao, Siyu Li, Yixin Liu, Zhiling Yan, Yutong Dai, Philip S. Yu, and Lichao Sun. 2018. A Comprehensive
Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT. J. ACM 37, 4,
Article 111 (August 2018), 44 pages. https://doi.org/XXXXXXX.XXXXXXX
∗
Incoming Ph.D. student at Lehigh University.
Authors’ addresses: Yihan Cao, yihanc@andrew.cmu.edu, Lehigh University & Carnegie Mellon University, Pittsburgh, PA,
USA; Siyu Li, applicantlisiyu@hotmail.com, Lehigh University, Bethlehem, PA, USA; Yixin Liu, lis221@lehigh.edu, Lehigh
University, Bethlehem, PA, USA; Zhiling Yan, zhilingyan724@outlook.com, Lehigh University, Bethlehem, PA, USA; Yutong
Dai, lis221@lehigh.edu, Lehigh University, Bethlehem, PA, USA; Philip S. Yu, University of Illinois at Chicago, Chicago,
Illinois, USA, psyu@uic.edu; Lichao Sun, lis221@lehigh.edu, Lehigh University, Bethlehem, PA, USA.
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https://doi.org/XXXXXXX.XXXXXXX
J. ACM, Vol. 37, No. 4, Article 111. Publication date: August 2018.
arXiv:2303.04226v1 [cs.AI] 7 Mar 2023