Evaluating a Synthetic Image Dataset Generated with Stable Diffusion
This work provides a synthetic dataset for data augmentation in machine learning, but it is incremental as it applies an existing method to new data without major innovations.
The study generated a synthetic image dataset using Stable Diffusion based on WordNet concepts to evaluate its capabilities for data augmentation and model investigation, finding it produces correct images for many concepts but with varied representations and issues with specific concepts.
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.