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arXiv · 2405.02698

Stable Diffusion Dataset Generation for Downstream Classification Tasks

Eugenio Lomurno·Matteo D'Oria·Matteo Matteucci
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Abstract

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.

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Eugenio Lomurno, Matteo D'Oria, Matteo Matteucci. 2024-05-04. Stable Diffusion Dataset Generation for Downstream Classification Tasks. https://arxiv.org/abs/2405.02698

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