Stable Diffusion Dataset Generation for Downstream Classification Tasks

arXiv:2405.02698v1 Announce Type: new
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 …

abstract advances artificial artificial intelligence arxiv classification cs.ai cs.cv cs.lg data dataset dataset generation datasets diffusion fine-tuning generative generative artificial intelligence intelligence optimisation paper quality stable diffusion synthetic synthetic data tasks transfer transfer learning type utility world