A Comprehensive Survey of Image Generation Models Based on Deep Learning

Jun Li (), Chenyang Zhang, Wei Zhu and Yawei Ren
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Jun Li: Beijing Information Science and Technology University
Chenyang Zhang: Beijing Information Science and Technology University
Wei Zhu: Beijing Information Science and Technology University
Yawei Ren: Beijing Information Science and Technology University

Annals of Data Science, 2025, vol. 12, issue 1, No 6, 170 pages

Abstract: Abstract In recent years, generative artificial intelligence has been developing rapidly. In the image domain, image generation models based on deep learning have made remarkable achievements. Early frameworks for image generation models were dominated by generative adversarial networks (GANs) and variational autoencoders (VAEs). Nowadays, large-scale generative models based on diffusion models have become mainstream, and the quality of their generated images is significantly improved. We will review the research and development of image generation models and delve into the significant progress made in the field in recent years. Initially, we revisit the development of traditional image generation models like GANs and VAEs, emphasizing their contributions and challenges. We also introduce diffusion models, which have received much attention in the field of image generation due to their unique generative process and excellent generative performance. Subsequently, we emphasized the large vision models with SAM as the focal point. We also pay special attention to large-scale generative models like Stable Diffusion, which have demonstrated unprecedented capabilities in high-quality image generation tasks. Additionally, we explore target models and respective fine-tuning methods for domain-oriented image generation tasks, predicts future directions in image generation, and proposes potential research focuses and challenges.

Keywords: Deep learning; Generative models; Generative adversarial networks; Diffusion models; Decision making (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s40745-024-00544-1

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