mlsanityData to 7 Oct 2026

Effective Data Augmentation With Diffusion Models

arXiv 2302.07944 ↗First posted 7 Feb 2023cs.CV cs.AIcited by 282

On the map: Diffusion models → Image generation, text-to-image and editing

Abstract (arXiv)

Data augmentation is one of the most prevalent tools in deep learning, underpinning many recent advances, including those from classification, generative models, and representation learning. The standard approach to data augmentation combines simple transformations like rotations and flips to generate new images from existing ones. However, these new images lack diversity along key semantic axes present in the data. Current augmentations cannot alter the high-level semantic attributes, such as animal species present in a scene, to enhance the diversity of data. We address the lack of diversity in data augmentation with image-to-image transformations parameterized by pre-trained text-to-image diffusion models. Our method edits images to change their semantics using an off-the-shelf diffusion model, and generalizes to novel visual concepts from a few labelled examples. We evaluate our approach on few-shot image classification tasks, and on a real-world weed recognition task, and observe an improvement in accuracy in tested domains.

Key facts. Cited by 282 papers in the map (data through 7 Oct 2026). Placed in Image generation, text-to-image and editing, one of 14 directions in Diffusion models. Builds on 44 of its 74 references that are in the map. First posted on arXiv 7 Feb 2023.

Builds on

44 of 74 references.

Closest ancestors · 5

Same direction, newest first

  1. 2022Null-text Inversion for Editing Real Images using Guided Diffusion Models17 Nov
  2. 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding23 May
  3. 2022Hierarchical Text-Conditional Image Generation with CLIP Latents13 Apr
  4. 2021GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models20 Dec
  5. 2021Palette: Image-to-Image Diffusion Models10 Nov

Other references · 39

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  3. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  4. 2020Denoising Diffusion Implicit Modelscited by 9,655
  5. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  6. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  7. 2022Classifier-Free Diffusion Guidancecited by 5,717
  8. 2020Training data-efficient image transformers & distillation through attentioncited by 4,560
  9. 2022LAION-5B: An open large-scale dataset for training next generation image-text modelscited by 4,131
  10. 2021On the Opportunities and Risks of Foundation Modelscited by 3,961
  11. 2021Prefix-Tuning: Optimizing Continuous Prompts for Generationcited by 3,878
  12. 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,736
27 more
  1. 2019CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Featurescited by 2,875
  2. 2018Large Scale GAN Training for High Fidelity Natural Image Synthesiscited by 2,609
  3. 2022Locating and Editing Factual Associations in GPTcited by 2,609
  4. 2022Prompt-to-Prompt Image Editing with Cross Attention Controlcited by 2,409
  5. 2019RandAugment: Practical automated data augmentation with a reduced search spacecited by 2,261
  6. 2022An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversioncited by 2,231
  7. 2021SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equationscited by 1,903
  8. 2013Fine-Grained Visual Classification of Aircraftcited by 1,701
  9. 2022RePaint: Inpainting using Denoising Diffusion Probabilistic Modelscited by 1,419
  10. 2019Generating Diverse High-Fidelity Images with VQ-VAE-2cited by 1,376
  11. 2017Revisiting Unreasonable Effectiveness of Data in Deep Learning Eracited by 916
  12. 2023Extracting Training Data from Diffusion Modelscited by 723
  13. 2017The Effectiveness of Data Augmentation in Image Classification using Deep Learningcited by 368
  14. 2020Differentiable Augmentation for Data-Efficient GAN Trainingcited by 361
  15. 2021Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learningcited by 359
  16. 2022Is synthetic data from generative models ready for image recognition?cited by 320
  17. 2017Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitrocited by 280
  18. 2017Data Augmentation Generative Adversarial Networkscited by 278
  19. 2021DatasetGAN: Efficient Labeled Data Factory with Minimal Human Effortcited by 264
  20. 2021Generative Models as a Data Source for Multiview Representation Learningcited by 104
  21. 2020Image GANs meet Differentiable Rendering for Inverse Graphics and Interpretable 3D Neural Renderingcited by 101
  22. 2019This dataset does not exist: training models from generated imagescited by 65
  23. 2019Data Augmentation Using GANscited by 58
  24. 2017A Bayesian Data Augmentation Approach for Learning Deep Modelscited by 50
  25. 2019Effective Data Augmentation with Multi-Domain Learning GANscited by 11
  26. 2020EC-GAN: Low-Sample Classification using Semi-Supervised Algorithms and GANscited by 10
  27. 2020Fine-grained Image-to-Image Transformation towards Visual Recognitioncited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 53; 2024: 101; 2025: 80; 2026: 48.

282 papers.

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  1. 2023Synthetic Data from Diffusion Models Improves ImageNet Classificationcited by 326
  2. 2024Rephrasing the Web: A Recipe for Compute and Data-Efficient Language Modelingcited by 156
  3. 2024URDFormer: A Pipeline for Constructing Articulated Simulation Environments from Real-World Imagescited by 106
  4. 2023Diversify Your Vision Datasets with Automatic Diffusion-Based Augmentationcited by 100
  5. 2023Global Structure-Aware Diffusion Process for Low-Light Image Enhancementcited by 92
  6. 2024Understanding Hallucinations in Diffusion Models through Mode Interpolationcited by 87
  7. 2024DiffuseMix: Label-Preserving Data Augmentation with Diffusion Modelscited by 78
  8. 2023Dream the Impossible: Outlier Imagination with Diffusion Modelscited by 77
  9. 2023FreeMask: Synthetic Images with Dense Annotations Make Stronger Segmentation Modelscited by 61
  10. 2025Synthetic Data Generation Using Large Language Models: Advances in Text and Codecited by 61
  11. 2023EditVal: Benchmarking Diffusion Based Text-Guided Image Editing Methodscited by 52
  12. 2024Balancing Act: Distribution-Guided Debiasing in Diffusion Modelscited by 50
38 more
  1. 2023DGInStyle: Domain-Generalizable Semantic Segmentation with Image Diffusion Models and Stylized Semantic Controlcited by 44
  2. 2023COCO-Counterfactuals: Automatically Constructed Counterfactual Examples for Image-Text Pairscited by 39
  3. 2024A Comprehensive Survey on Data Augmentationcited by 38
  4. 2024Diffusion Feedback Helps CLIP See Bettercited by 37
  5. 2024Enhance Image Classification via Inter-Class Image Mixup with Diffusion Modelcited by 34
  6. 2024LTGC: Long-tail Recognition via Leveraging LLMs-driven Generated Contentcited by 33
  7. 2024Beyond the Contact: Discovering Comprehensive Affordance for 3D Objects from Pre-trained 2D Diffusion Modelscited by 32
  8. 2024A Survey on Data Augmentation in Large Model Eracited by 31
  9. 2025InSTA: Towards Internet-Scale Training For Agentscited by 31
  10. 2024AutoGeo: Automating Geometric Image Dataset Creation for Enhanced Geometry Understandingcited by 28
  11. 2023Diffusion-TTA: Test-time Adaptation of Discriminative Models via Generative Feedbackcited by 27
  12. 2023Toward Understanding Generative Data Augmentationcited by 25
  13. 2023Enhancing Multimodal Compositional Reasoning of Visual Language Models with Generative Negative Miningcited by 25
  14. 2024DreamDA: Generative Data Augmentation with Diffusion Modelscited by 24
  15. 2024DiffClass: Diffusion-Based Class Incremental Learningcited by 23
  16. 2024GTA: Generative Trajectory Augmentation with Guidance for Offline Reinforcement Learningcited by 22
  17. 2023Boosting Human-Object Interaction Detection with Text-to-Image Diffusion Modelcited by 21
  18. 2023Is Synthetic Data From Diffusion Models Ready for Knowledge Distillation?cited by 21
  19. 2024Distribution-Aware Data Expansion with Diffusion Modelscited by 20
  20. 2023CHORUS: Learning Canonicalized 3D Human-Object Spatial Relations from Unbounded Synthesized Imagescited by 19
  21. 2023APoLLo: Unified Adapter and Prompt Learning for Vision Language Modelscited by 19
  22. 2023Generating Images with 3D Annotations Using Diffusion Modelscited by 18
  23. 2024Stylus: Automatic Adapter Selection for Diffusion Modelscited by 18
  24. 2024ODGEN: Domain-specific Object Detection Data Generation with Diffusion Modelscited by 17
  25. 2023Improving Generalization and Data Efficiency with Diffusion in Offline Multi-agent RLcited by 15
  26. 2025Deep Learning and Foundation Models for Weather Prediction: A Surveycited by 15
  27. 2023Image retrieval outperforms diffusion models on data augmentationcited by 14
  28. 2024GenMix: Effective Data Augmentation with Generative Diffusion Model Image Editingcited by 14
  29. 2023Data Augmentation for Seizure Prediction with Generative Diffusion Modelcited by 13
  30. 2023AI-Generated Images as Data Source: The Dawn of Synthetic Eracited by 13
  31. 2024Low-Resource Vision Challenges for Foundation Modelscited by 13
  32. 2024DiffuMatting: Synthesizing Arbitrary Objects with Matting-level Annotationcited by 13
  33. 2024CTSyn: A Foundation Model for Cross Tabular Data Generationcited by 13
  34. 2023SYNAuG: Exploiting Synthetic Data for Data Imbalance Problemscited by 12
  35. 2023Diffusion Based Augmentation for Captioning and Retrieval in Cultural Heritagecited by 12
  36. 2024Advancing Fine-Grained Classification by Structure and Subject Preserving Augmentationcited by 12
  37. 2024SK-VQA: Synthetic Knowledge Generation at Scale for Training Context-Augmented Multimodal LLMscited by 12
  38. 2024Towards Effective Data-Free Knowledge Distillation via Diverse Diffusion Augmentationcited by 12

50 most cited of 282 All 282 (JSON)

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  1. 2024Advances in Diffusion Models for Image Data Augmentation: A Review of Methods, Models, Evaluation Metrics and Future Research Directions
  2. 2024Semantic Augmentation in Images using Language
  3. 2023Stable Diffusion for Data Augmentation in COCO and Weed Datasets
  4. 2022Cap2Aug: Caption guided Image to Image data Augmentation
  5. 2023Semantic-Guided Generative Image Augmentation Method with Diffusion Models for Image Classification
  6. 2022Deep Data Augmentation for Weed Recognition Enhancement: A Diffusion Probabilistic Model and Transfer Learning Based Approach
  7. 2025Prompt-Free Conditional Diffusion for Multi-object Image Augmentation
  8. 2024A Simple Background Augmentation Method for Object Detection with Diffusion Model