[Paper Review] AI-Generated Images as Data Source: The Dawn of Synthetic Era

Zuhao Yang, Fangneng Zhan+3 more|arXiv (Cornell University)|Oct 3, 2023

Generative Adversarial Networks and Image Synthesis4 citations

TL;DR

This paper introduces AI-Generated Images as Data Sources (AIGS), proposing that synthetic images from advanced generative models like diffusion models and GANs can serve as scalable, privacy-preserving, and high-quality training data for visual intelligence tasks. The key contribution is a comprehensive analysis of AIGS's potential to overcome data scarcity, reduce annotation costs, and enhance model generalization across computer vision applications, while identifying critical challenges in evaluation, explainability, and 3D awareness.

ABSTRACT

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 .

Motivation & Objective

• To investigate the feasibility and advantages of using AI-generated images as training and testing data in visual intelligence.
• To address the limitations of traditional data collection, including high annotation costs, data scarcity, and privacy concerns.
• To analyze the role of generative models—especially diffusion models and GANs—in producing high-fidelity, scalable synthetic data.
• To examine the impact of AIGS on downstream tasks such as object detection, semantic segmentation, depth estimation, and autonomous driving simulation.
• To identify open challenges in evaluation metrics, model explainability, and 3D-aware generation for future research.

Proposed method

• Leveraging diffusion models and GANs to generate high-resolution, photorealistic images with controlled semantics and diverse attributes.
• Using neural radiance fields (NeRF) and neural rendering to synthesize multi-view consistent 3D-aware images for geometry-sensitive tasks.
• Applying text-to-image (T2I) models with CLIP-based alignment to ensure semantic fidelity between prompts and generated images.
• Employing automatic label acquisition through prompt engineering and latent space manipulation in generative models.
• Designing synthetic datasets via prompt-based data augmentation, including edge cases and rare scenarios.
• Integrating AIGS into benchmarking pipelines for model validation, robustness testing, and domain generalization.

Experimental results

Research questions

• RQ1 To what extent can AI-generated images replace real-world data in training deep learning models for visual intelligence tasks?
• RQ2 How do AIGS-based datasets compare to real data in terms of model generalization, robustness, and performance across diverse vision tasks?
• RQ3 What are the key limitations of current evaluation metrics for AI-generated images, and how can they be improved for reliable assessment?
• RQ4 How can 3D-aware generative models enhance the realism and utility of synthetic data for applications like autonomous driving and robotics?
• RQ5 What ethical, legal, and explainability challenges arise when deploying AIGS in real-world AI systems?

Key findings

• AIGS significantly reduces data collection and annotation costs while enabling rapid generation of large-scale, diverse, and edge-case-rich datasets.
• AI-generated images from diffusion models and GANs show smaller domain gaps compared to graphics-engine-based synthetic data, improving model generalization.
• The use of pre-trained CLIP and perceptual metrics like DreamSim enables more faithful evaluation of image-text alignment and visual quality.
• Despite progress, current AIGS methods lack explainability for outliers and extreme cases, highlighting a need for improved interpretability frameworks.
• 3D-aware AIGS using NeRF and neural rendering shows promise but remains limited by challenges in learning complex scene geometry from unposed 2D images.
• The scalability and controllability of AIGS make it a transformative paradigm for data-scarce domains such as medical imaging and autonomous systems.