Stable Diffusion Dataset Generation for Downstream Classification Tasks

• URL: http://arxiv.org/abs/2405.02698v1
• Date: Sat, 4 May 2024 15:37:22 GMT
• Title: Stable Diffusion Dataset Generation for Downstream Classification Tasks
• Authors: Eugenio Lomurno, Matteo D'Oria, Matteo Matteucci,
• Abstract summary: This paper explores the adaptation of the Stable Diffusion 2.0 model for generating synthetic datasets. 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.
• Score: 4.470499157873342
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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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