Defining and Quantifying Creative Behavior in Popular Image Generators
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Abstract (arXiv)
Creativity of generative AI models has been a subject of scientific debate in the last years, without a conclusive answer. In this paper, we study creativity from a practical perspective and introduce quantitative measures that help the user to choose a suitable AI model for a given task. We evaluated our measures on a number of popular image-to-image generation models, and the results of this suggest that our measures conform to human intuition.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 8 of its 21 references that are in the map. First posted on arXiv 7 May 2025.
Builds on
8 of 21 references.
References · 8
- 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,177
- 2024Scaling Rectified Flow Transformers for High-Resolution Image Synthesiscited by 4,181
- 2023Kosmos-2: Grounding Multimodal Large Language Models to the Worldcited by 1,089
- 2023Kandinsky: an Improved Text-to-Image Synthesis with Image Prior and Latent Diffusioncited by 114
- 2024Is Temperature the Creativity Parameter of Large Language Models?cited by 93
- 2022The Creativity of Text-to-Image Generationcited by 73
- 2023Probing the Creativity of Large Language Models: Can models produce divergent semantic association?cited by 33
- 2024AIGCs Confuse AI Too: Investigating and Explaining Synthetic Image-induced Hallucinations in Large Vision-Language Modelscited by 6
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Citing papers per year: 2026: 1.
1 paper.