mlsanityData to 14 Oct 2026

AI-Generated Images as Data Source: The Dawn of Synthetic Era

arXiv 2310.01830 ↗First posted 3 Oct 2023cs.CVcited by 13

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Abstract (arXiv)

The advancement of visual intelligence is intrinsically tethered to the availability of large-scale data. In parallel, generative Artificial Intelligence (AI) has unlocked the potential to create synthetic images that closely resemble real-world photographs. This prompts a compelling inquiry: how much visual intelligence could benefit from the advance of generative AI? This paper explores the innovative concept of harnessing these AI-generated images as new data sources, reshaping traditional modeling paradigms in visual intelligence. In contrast to real data, AI-generated data exhibit remarkable advantages, including unmatched abundance and scalability, the rapid generation of vast datasets, and the effortless simulation of edge cases. Built on the success of generative AI models, we examine the potential of their generated data in a range of applications, from training machine learning models to simulating scenarios for computational modeling, testing, and validation. We probe the technological foundations that support this groundbreaking use of generative AI, engaging in an in-depth discussion on the ethical, legal, and practical considerations that accompany this transformative paradigm shift. Through an exhaustive survey of current technologies and applications, this paper presents a comprehensive view of the synthetic era in visual intelligence. A project associated with this paper can be found at https://github.com/mwxely/AIGS .

Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). Builds on 210 of its 255 references that are in the map. First posted on arXiv 3 Oct 2023.

Builds on

210 of 255 references.

References · 210

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Citing papers per year: 2023: 3; 2024: 3; 2025: 3; 2026: 4.

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  1. 2024Mapping the Ethics of Generative AI: A Comprehensive Scoping Reviewcited by 19
  2. 2025Alignment Quality Index (AQI) : Beyond Refusals: AQI as an Intrinsic Alignment Diagnostic via Latent Geometry, Cluster Divergence, and Layer wise Pooled Representationscited by 8
  3. 2024AIGCs Confuse AI Too: Investigating and Explaining Synthetic Image-induced Hallucinations in Large Vision-Language Modelscited by 6
  4. 2025A Critical Assessment of Modern Generative Models' Ability to Replicate Artistic Stylescited by 6
  5. 2023ScribbleGen: Generative Data Augmentation Improves Scribble-supervised Semantic Segmentationcited by 5
  6. 2026Visual Generation in the New Era: An Evolution from Atomic Mapping to Agentic World Modelingcited by 5
  7. 2025Synthetic Human Action Video Data Generation with Pose Transfercited by 2
  8. 2026Improving Text-to-Image Generation with Intrinsic Self-Confidence Rewardscited by 2
  9. 2023DatasetNeRF: Efficient 3D-aware Data Factory with Generative Radiance Fieldscited by 1
  10. 2023A Low-Overhead Incorporation-Extrapolation based Few-Shot CSI Feedback Framework for Massive MIMO Systemscited by 1
  11. 2024Paint Outside the Box: Synthesizing and Selecting Training Data for Visual Groundingcited by 1
  12. 2026Beyond Cropping and Rotation: Automated Evolution of Powerful Task-Specific Augmentations with Generative Modelscited by 0
1 more
  1. 2026Impact of Synthetic Lesional MR Images in Automated Focal Cortical Dysplasia Detection in Low-Data Scenarioscited by 0