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

By Yihan Cao, Siyu Li, Zhiling Yan, Yutong Dai, Philip S. Yu, Lichao Sun. arXiv.org, 2023

821 citations recorded by the source corpus.

Recently, ChatGPT, along with DALL-E-2 and Codex,has been gaining significant 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 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 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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/a7b3a868a80dbe97689135c99b1a6b6e10dcdfe5

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Citations in the corpus

When large language models meet personalization: perspectives of challenges and opportunities (2023, neutral): “AIGC [251] involves the creation of digital content, such as images, music and natural language through AI models, with the aim of making the content creation process more efficient and accessible.”
Generative artificial intelligence: a systematic review and applications (2024, neutral): “Generative AI even has found its way into education[19, 34].”
Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models (2024, background): “The recent progress in Artificial Intelligence Generated Content (AIGC) technologies has democratized content creation, enabling users to generate desired content through simple textual instructions [11].”
AI literacy and its implications for prompt engineering strategies (2024, background): “As a result, LLMs have the potential to significantly advance higher education by enabling tailored learning experiences (Cao et al., 2023), enhancing group discussions (H. Nguyen, 2023), improving educational outcomes and learning strategies, and providing opportunities to be incorporated into…”
Machine Learning for Synthetic Data Generation: a Review (2023, background): “This capacity extends beyond simple question-answer pairs to crafting whole articles, stories, or technical explanations on a wide range of topics, thus creating a novel way of producing blog posts, articles, social media content, and more [118], [119].”
AudioLDM 2: Learning Holistic Audio Generation With Self-Supervised Pretraining (2023, background): “A RTIFICIAL intelligence generated content (AIGC) refers to any digital content such as images, videos, text, or audio that has been fully or partially created by an AI system without human involvement in the creative process [1].”
Exploring the Integration of Large Language Models in Industrial Test Maintenance Processes (2024, background): “Generative AI refers to machine learning models that produce content in the form of test, images, videos, or music in response to instructions delivered in the form of a prompt [21].”