Stable Diffusion Dataset Generation for Downstream Classification Tasks
Eugenio Lomurno, Matteo D'Oria, Matteo Matteucci · 2024
Recent advances in generative artificial intelligence have enabled the creation of high-quality synthetic data that closely mimics real-world data.This paper explores the adaptation of the Stable Diffusion 2.0 model for generating synthetic datasets, using Transfer Learning, Fine-Tuning and generation parameter optimisation techniques to improve the utility of the dataset for downstream classification tasks.We present a class-conditional version of the model that exploits a Class-Encoder and optimisation of key generation parameters.Our methodology led to synthetic datasets that, in a third of cases, produced models that outperformed those trained on real datasets. * This paper is supported by the FAIR (Future Artificial Intelligence Research) project, funded by the NextGenerationEU program within the PNRR-PE-AI scheme (M4C2, investment 1.3, line on Artificial Intelligence).1 The authors have contributed in equal measure.