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2023

Measuring the Success of Diffusion Models at Imitating Human Artists

Casper, Stephen, Guo, Zifan, Mogulothu, Shreya et al.

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Modern diffusion models have set the state-of-the-art in AI image generation.

Their success is due, in part, to training on Internet-scale data which often includes copyrighted work.
This prompts questions about the extent to which these models learn from, imitate, or copy the work of human artists.
This work suggests that tying copyright liability to the capabilities of the model may be useful given the evolving ecosystem of generative models.

Measuring the Success of Diffusion Models at Imitating Human Artists · Around

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Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., and Ganguli, S · 2015
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Copyright’s framing problem
Kaminski, M. E. and Rub, G. A · 2017
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