AI-Generated Images as Data Source: The Dawn of Synthetic Era
- URL: http://arxiv.org/abs/2310.01830v3
- Date: Mon, 23 Oct 2023 08:57:58 GMT
- Title: AI-Generated Images as Data Source: The Dawn of Synthetic Era
- Authors: Zuhao Yang, Fangneng Zhan, Kunhao Liu, Muyu Xu, Shijian Lu
- Abstract summary: generative AI has unlocked the potential to create synthetic images that closely resemble real-world photographs.
This paper explores the innovative concept of harnessing these AI-generated images as new data sources.
In contrast to real data, AI-generated data exhibit remarkable advantages, including unmatched abundance and scalability.
- Score: 61.879821573066216
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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 .
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