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.
翻译:AI的一个重要里程碑是开发能够产生与人类的图纸无法区分的图纸的算法。 在这里,我们调整了Boutin等人(2022年)的“多样性与可识别性”评分框架,发现一发扩散模型确实开始缩小人类与机器之间的差距。然而,我们采用对个体样本原创性的精细测量方法,表明加强对传播模型的指导有助于提高其图画的人道性,但是它们仍然不能接近人类图画的原创性和可识别性。通过在线心理物理实验收集的人类类别诊断特征与扩散模型得出的特征相比,表明人类依赖较少和更加局部的特征。总体而言,我们的研究表明,扩散模型极大地帮助提高了机器生成的图画的质量;然而,人类与机器之间的差距仍然存在,部分可以通过视觉战略的差异来解释。