Diffusion Models as Artists: Are we Closing the Gap between Humans and Machines?

Authors: Boutin, V., Fel, T., Singhal, L., Mukherji, R., Nagaraj, A., Colin, J. , Serre, T.

External link: https://proceedings.mlr.press/v202/boutin23a.html
Publication: Proceedings of the International Conference on Machine Learning (ICML), p. 2953-3002, 2023
DOI: https://arxiv.org/abs/2301.11722
PDF: Click here for the PDF paper

An important milestone for AI is the development of algorithms that can produce drawings that are indistinguishable from those of humans. Here, we adapt the “diversity vs. recognizability” scoring framework from Boutin et al., 2022 and find that one-shot diffusion models have indeed started to close the gap between humans and machines. However, using a finer-grained measure of the originality of individual samples, we show that strengthening the guidance of diffusion models helps improve the humanness of their drawings, but they still fall short of approximating the originality and recognizability of human drawings. Comparing human category diagnostic features, collected through an online psychophysics experiment, against those derived from diffusion models reveals that humans rely on fewer and more localized features. Overall, our study suggests that diffusion models have significantly helped improve the quality of machine-generated drawings; however, a gap between humans and machines remains – in part explainable by discrepancies in visual strategies.