AIGC: a survey of AI-generated content technologies

As a novel content creation method, AI-generated content (AIGC) technology provides new solutions to the challenges of digital intelligence in the digital economy. AIGC utilizes artificial intelligence to assist or replace manual content creation, generating various forms of content such as text, images, audio, and video based on user needs. In recent years, the rapid development of deep learning models has significantly enhanced the generative capabilities of AIGC, making it a research hotspot in the field of artificial intelligence. As a fundamental technology, AIGC has enormous application potential but also faces certain limitations. This paper provides a comprehensive overview of AIGC technology. Firstly, it introduces the related algorithms of AIGC, including generative adversarial networks, variational autoencoder, diffusion models, and large language models; then it explores the applications of AIGC in generating different content forms; finally, it summarizes the challenges faced by AIGC technology and discusses future development trends.

Publication Details

Journal
SHILAP Revista de lepidopterología
Published
2025-01-01
Primary Topic
Generative Adversarial Networks and Image Synthesis
Type
article
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article

AIGC: a survey of AI-generated content technologies

SHILAP Revista de lepidopterología
Generative Adversarial Networks and Image Synthesis
article

AIGC: a survey of AI-generated content technologies

article en

Abstract

As a novel content creation method, AI-generated content (AIGC) technology provides new solutions to the challenges of digital intelligence in the digital economy. AIGC utilizes artificial intelligence to assist or replace manual content creation, generating various forms of content such as text, images, audio, and video based on user needs. In recent years, the rapid development of deep learning models has significantly enhanced the generative capabilities of AIGC, making it a research hotspot in the field of artificial intelligence. As a fundamental technology, AIGC has enormous application potential but also faces certain limitations. This paper provides a comprehensive overview of AIGC technology. Firstly, it introduces the related algorithms of AIGC, including generative adversarial networks, variational autoencoder, diffusion models, and large language models; then it explores the applications of AIGC in generating different content forms; finally, it summarizes the challenges faced by AIGC technology and discusses future development trends.

SHILAP Revista de lepidopterología
Openalex Percentile: Top 47%
Generative Adversarial Networks and Image Synthesis
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