We generate synthetic images with the "Stable Diffusion" image generation model using the Wordnet taxonomy and the definitions of concepts it contains. This synthetic image database can be used as training data for data augmentation in machine learning applications, and it is used to investigate the capabilities of the Stable Diffusion model. Analyses show that Stable Diffusion can produce correct images for a large number of concepts, but also a large variety of different representations. The results show differences depending on the test concepts considered and problems with very specific concepts. These evaluations were performed using a vision transformer model for image classification.
翻译:我们用“稳定传播”图像生成模型,使用Wordnet分类法及其所载概念的定义生成合成图像。该合成图像数据库可以用作机器学习应用中数据增强的培训数据,并用来调查稳定传播模型的能力。分析显示,稳定传播可以为大量概念产生正确图像,但也可以产生多种不同的表达形式。结果显示根据所考虑的测试概念和非常具体的概念存在的问题而存在差异。这些评价是使用图像分类的视觉变压器模型进行的。