Stable Diffusion Dataset Generation for Downstream Classification Tasks
<|reference_start|>Stable Diffusion Dataset Generation for Downstream Classification Tasks: 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.<|reference_end|>
@article{lomurno2024stable,
    title={Stable Diffusion Dataset Generation for Downstream Classification Tasks},
    author={Eugenio Lomurno, Matteo D'Oria, Matteo Matteucci},
    journal={arXiv preprint arXiv:2405.02698},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.02698},
    primaryClass={cs.LG cs.AI cs.CV}
}