mlsanityData to 7 Oct 2026

Stable Diffusion Dataset Generation for Downstream Classification Tasks

arXiv 2405.02698 ↗First posted 4 May 2024cs.LG cs.AI cs.CVcited by 9

On the map: Diffusion models → Diffusion foundations and sampling

Abstract (arXiv)

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.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models. Builds on 6 of its 9 references that are in the map. First posted on arXiv 4 May 2024.

Builds on

6 of 9 references.

References · 6

  1. 2019Optuna: A Next-generation Hyperparameter Optimization Frameworkcited by 2,455
  2. 2022Dataset Distillation by Matching Training Trajectoriescited by 437
  3. 2022Fake it till you make it: Learning transferable representations from synthetic ImageNet clonescited by 171
  4. 2019Classification Accuracy Score for Conditional Generative Modelscited by 134
  5. 2021SGDE: Secure Generative Data Exchange for Cross-Silo Federated Learningcited by 15
  6. 2023Bridging the Gap: Enhancing the Utility of Synthetic Data via Post-Processing Techniquescited by 11

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 5; 2026: 2.

9 papers.

  1. 2024Federated Knowledge Recycling: Privacy-Preserving Synthetic Data Sharingcited by 7
  2. 2026When Pretty Isn't Useful: Investigating Why Modern Text-to-Image Models Fail as Reliable Training Data Generatorscited by 6
  3. 2024Synthetic Image Learning: Preserving Performance and Preventing Membership Inference Attackscited by 5
  4. 2025Your Image Generator Is Your New Private Datasetcited by 2
  5. 2025Language-Informed Hyperspectral Image Synthesis for Imbalanced-Small Sample Classification via Semi-Supervised Conditional Diffusion Modelcited by 1
  6. 2026PolyGen: Fully Synthetic Vision-Language Training via Multi-Generator Ensemblescited by 1
  7. 2025Neuro-Symbolic Scene Graph Conditioning for Synthetic Image Dataset Generationcited by 0
  8. 2025Improving Physical Object State Representation in Text-to-Image Generative Systemscited by 0
  9. 2025On Approaches to Building Surrogate ODE Models for Diffusion Bridgescited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2022Evaluating a Synthetic Image Dataset Generated with Stable Diffusion
  2. 2023On the Limitation of Diffusion Models for Synthesizing Training Datasets
  3. 2023Training on Thin Air: Improve Image Classification with Generated Data
  4. 2025Boosting Statistic Learning with Synthetic Data from Pretrained Large Models
  5. 2024Generative Modeling with Diffusion
  6. 2026Representation-Conditioned Diffusion Models for Guided Training Data Generation
  7. 2024Mind the Gap Between Synthetic and Real: Utilizing Transfer Learning to Probe the Boundaries of Stable Diffusion Generated Data
  8. 2022Hierarchically branched diffusion models leverage dataset structure for class-conditional generation