mlsanityData to 7 Oct 2026

Is synthetic data from generative models ready for image recognition?

arXiv 2210.07574 ↗First posted 14 Oct 2022cs.CVcited by 320

Not on the map. Why?

Cited by 104 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Recent text-to-image generation models have shown promising results in generating high-fidelity photo-realistic images. Though the results are astonishing to human eyes, how applicable these generated images are for recognition tasks remains under-explored. In this work, we extensively study whether and how synthetic images generated from state-of-the-art text-to-image generation models can be used for image recognition tasks, and focus on two perspectives: synthetic data for improving classification models in data-scarce settings (i.e. zero-shot and few-shot), and synthetic data for large-scale model pre-training for transfer learning. We showcase the powerfulness and shortcomings of synthetic data from existing generative models, and propose strategies for better applying synthetic data for recognition tasks. Code: https://github.com/CVMI-Lab/SyntheticData.

Key facts. Cited by 320 papers in the map (data through 7 Oct 2026). Builds on 54 of its 77 references that are in the map. First posted on arXiv 14 Oct 2022.

Builds on

54 of 77 references.

References · 54

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38 more
  1. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,539
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50 most cited of 54 All 54 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 4; 2023: 96; 2024: 110; 2025: 79; 2026: 31.

320 papers.

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  1. 2023Synthetic Data from Diffusion Models Improves ImageNet Classificationcited by 326
  2. 2023DreamSim: Learning New Dimensions of Human Visual Similarity using Synthetic Datacited by 322
  3. 2023Effective Data Augmentation With Diffusion Modelscited by 282
  4. 2023Multimodal Foundation Models: From Specialists to General-Purpose Assistantscited by 247
  5. 2023Self-Consuming Generative Models Go MADcited by 239
  6. 2023StableRep: Synthetic Images from Text-to-Image Models Make Strong Visual Representation Learnerscited by 177
  7. 2022Fake it till you make it: Learning transferable representations from synthetic ImageNet clonescited by 171
  8. 2023DiffuMask: Synthesizing Images with Pixel-level Annotations for Semantic Segmentation Using Diffusion Modelscited by 162
  9. 2023RS5M and GeoRSCLIP: A Large Scale Vision-Language Dataset and A Large Vision-Language Model for Remote Sensingcited by 150
  10. 2024A Tale of Tails: Model Collapse as a Change of Scaling Lawscited by 127
  11. 2023DatasetDM: Synthesizing Data with Perception Annotations Using Diffusion Modelscited by 122
  12. 2022SuS-X: Training-Free Name-Only Transfer of Vision-Language Modelscited by 118
38 more
  1. 2024Eyes Closed, Safety On: Protecting Multimodal LLMs via Image-to-Text Transformationcited by 114
  2. 2023Scaling Laws of Synthetic Images for Model Training ... for Nowcited by 107
  3. 2024UniScene: Unified Occupancy-centric Driving Scene Generationcited by 102
  4. 2023Synthetic Experience Replaycited by 100
  5. 2023Diversify Your Vision Datasets with Automatic Diffusion-Based Augmentationcited by 100
  6. 2023Waffling around for Performance: Visual Classification with Random Words and Broad Conceptscited by 100
  7. 2023Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limitationscited by 96
  8. 2023Diffusion Model as Representation Learnercited by 95
  9. 2023Improving Multimodal Datasets with Image Captioningcited by 93
  10. 2023Leaving Reality to Imagination: Robust Classification via Generated Datasetscited by 91
  11. 2023A Complete Survey on Generative AI (AIGC): Is ChatGPT from GPT-4 to GPT-5 All You Need?cited by 84
  12. 2023Diversity is Definitely Needed: Improving Model-Agnostic Zero-shot Classification via Stable Diffusioncited by 81
  13. 2023Efficient Dataset Distillation via Minimax Diffusioncited by 81
  14. 2023Generating images of rare concepts using pre-trained diffusion modelscited by 78
  15. 2023Dream the Impossible: Outlier Imagination with Diffusion Modelscited by 77
  16. 2023Fine-tuning Multimodal LLMs to Follow Zero-shot Demonstrative Instructionscited by 76
  17. 2024No "Zero-Shot" Without Exponential Data: Pretraining Concept Frequency Determines Multimodal Model Performancecited by 75
  18. 2023Learning Vision from Models Rivals Learning Vision from Datacited by 71
  19. 2024SynthCLIP: Are We Ready for a Fully Synthetic CLIP Training?cited by 64
  20. 2024DreamClear: High-Capacity Real-World Image Restoration with Privacy-Safe Dataset Curationcited by 64
  21. 2022Expanding Small-Scale Datasets with Guided Imaginationcited by 61
  22. 2023FreeMask: Synthetic Images with Dense Annotations Make Stronger Segmentation Modelscited by 61
  23. 2023GeoDiffusion: Text-Prompted Geometric Control for Object Detection Data Generationcited by 56
  24. 2023DiffusionSeg: Adapting Diffusion Towards Unsupervised Object Discoverycited by 53
  25. 2023Training on Thin Air: Improve Image Classification with Generated Datacited by 50
  26. 2023Synthetic Datasets for Autonomous Driving: A Surveycited by 48
  27. 2023DGInStyle: Domain-Generalizable Semantic Segmentation with Image Diffusion Models and Stylized Semantic Controlcited by 44
  28. 2024Beyond Model Collapse: Scaling Up with Synthesized Data Requires Verificationcited by 43
  29. 2024SatSynth: Augmenting Image-Mask Pairs through Diffusion Models for Aerial Semantic Segmentationcited by 41
  30. 2024RF-Diffusion: Radio Signal Generation via Time-Frequency Diffusioncited by 41
  31. 2024Is Synthetic Data all We Need? Benchmarking the Robustness of Models Trained with Synthetic Imagescited by 41
  32. 2023Prompting Hard or Hardly Prompting: Prompt Inversion for Text-to-Image Diffusion Modelscited by 40
  33. 2024A Survey on Data Synthesis and Augmentation for Large Language Modelscited by 40
  34. 2023COCO-Counterfactuals: Automatically Constructed Counterfactual Examples for Image-Text Pairscited by 39
  35. 2023Explore the Power of Synthetic Data on Few-shot Object Detectioncited by 38
  36. 2023Diffusion Models and Semi-Supervised Learners Benefit Mutually with Few Labelscited by 37
  37. 2023EventCLIP: Adapting CLIP for Event-based Object Recognitioncited by 36
  38. 2023One-shot Unsupervised Domain Adaptation with Personalized Diffusion Modelscited by 35

50 most cited of 320 All 320 (JSON)