DiffuGen: Adaptable Approach for Generating Labeled Image Datasets using Stable Diffusion Models
This addresses the need for efficient dataset generation in computer vision, but it appears incremental as it builds on existing stable diffusion models with new labeling techniques.
The paper tackles the problem of generating high-quality labeled image datasets, which is time-consuming and costly manually, by introducing DiffuGen, an adaptable approach using stable diffusion models that efficiently creates datasets with versatile label generation, achieving unspecified quality improvements.
Generating high-quality labeled image datasets is crucial for training accurate and robust machine learning models in the field of computer vision. However, the process of manually labeling real images is often time-consuming and costly. To address these challenges associated with dataset generation, we introduce "DiffuGen," a simple and adaptable approach that harnesses the power of stable diffusion models to create labeled image datasets efficiently. By leveraging stable diffusion models, our approach not only ensures the quality of generated datasets but also provides a versatile solution for label generation. In this paper, we present the methodology behind DiffuGen, which combines the capabilities of diffusion models with two distinct labeling techniques: unsupervised and supervised. Distinctively, DiffuGen employs prompt templating for adaptable image generation and textual inversion to enhance diffusion model capabilities.