论文标题
评估具有稳定扩散生成的合成图像数据集
Evaluating a Synthetic Image Dataset Generated with Stable Diffusion
论文作者
论文摘要
我们使用WordNet分类法及其包含的概念的定义生成使用“稳定扩散”图像生成模型的合成图像。此合成图像数据库可用作机器学习应用中数据增强的培训数据，并用于研究稳定扩散模型的功能。 分析表明，稳定的扩散可以为大量概念产生正确的图像，但也可以产生各种不同表示的图像。结果表明，取决于所考虑的测试概念以及具有非常具体概念的问题。这些评估是使用视觉变压器模型进行图像分类进行的。
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.
