# DreamDA: Generative Data Augmentation with Diffusion Models

arXiv: https://arxiv.org/abs/2403.12803 · HTML: https://mlsanity.com/paper/2403.12803/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Placed in Image generation, text-to-image and editing, one of 14 directions in Diffusion models.
- Builds on 50 of its 61 references that are in the map.
- First posted on arXiv 19 Mar 2024.

## Placed in

- Diffusion models / [Image generation, text-to-image and editing](https://mlsanity.com/diffusion/image-generation-text-to-image-and-editing/)

## Builds on (most cited first, 30 of 50)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022)
- [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021)
- [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021)
- [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://mlsanity.com/paper/1905.04899/) (2019)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [Prompt-to-Prompt Image Editing with Cross Attention Control](https://mlsanity.com/paper/2208.01626/) (2022)
- [RandAugment: Practical automated data augmentation with a reduced search space](https://mlsanity.com/paper/1909.13719/) (2019)
- [An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion](https://mlsanity.com/paper/2208.01618/) (2022)
- [SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations](https://mlsanity.com/paper/2108.01073/) (2021)
- [Random Erasing Data Augmentation](https://mlsanity.com/paper/1708.04896/) (2017)
- [Swin Transformer V2: Scaling Up Capacity and Resolution](https://mlsanity.com/paper/2111.09883/) (2021)
- [Null-text Inversion for Editing Real Images using Guided Diffusion Models](https://mlsanity.com/paper/2211.09794/) (2022)
- [Diffusion Models: A Comprehensive Survey of Methods and Applications](https://mlsanity.com/paper/2209.00796/) (2022)
- [On Distillation of Guided Diffusion Models](https://mlsanity.com/paper/2210.03142/) (2022)
- [Muse: Text-To-Image Generation via Masked Generative Transformers](https://mlsanity.com/paper/2301.00704/) (2023)
- [Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning](https://mlsanity.com/paper/1908.02983/) (2019)

## Built on by (most cited first, 24 of 24)

- [Inversion Circle Interpolation: Diffusion-based Image Augmentation for Data-scarce Classification](https://mlsanity.com/paper/2408.16266/) (2024)
- [Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery](https://mlsanity.com/paper/2507.04051/) (2025)
- [Context-guided Responsible Data Augmentation with Diffusion Models](https://mlsanity.com/paper/2503.10687/) (2025)
- [Diffusion Curriculum: Synthetic-to-Real Data Curriculum via Image-Guided Diffusion](https://mlsanity.com/paper/2410.13674/) (2024)
- [BOOD: Boundary-based Out-Of-Distribution Data Generation](https://mlsanity.com/paper/2508.00350/) (2025)
- [FlowComposer: Composable Flows for Compositional Zero-Shot Learning](https://mlsanity.com/paper/2603.16641/) (2026)
- [ED-SAM: An Efficient Diffusion Sampling Approach to Domain Generalization in Vision-Language Foundation Models](https://mlsanity.com/paper/2406.01432/) (2024)
- [A Diffusive Data Augmentation Framework for Reconstruction of Complex Network Evolutionary History](https://mlsanity.com/paper/2501.06485/) (2025)
- [D-Feat Occlusions: Diffusion Features for Robustness to Partial Visual Occlusions in Object Recognition](https://mlsanity.com/paper/2504.06432/) (2025)
- [SPIE: Semantic and Structural Post-Training of Image Editing Diffusion Models with AI feedback](https://mlsanity.com/paper/2504.12833/) (2025)
- [Do We Need All the Synthetic Data? Targeted Image Augmentation via Diffusion Models](https://mlsanity.com/paper/2505.21574/) (2025)
- [FlowCIR: Semantic Transport via Flow Matching for Zero-Shot Composed Image Retrieval](https://mlsanity.com/paper/2607.02284/) (2026)
- [Diffusion-based Data Augmentation and Knowledge Distillation with Generated Soft Labels Solving Data Scarcity Problems of SAR Oil Spill Segmentation](https://mlsanity.com/paper/2412.08116/) (2024)
- [ViCTr: Vital Consistency Transfer for Pathology Aware Image Synthesis](https://mlsanity.com/paper/2505.04963/) (2025)
- [LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance](https://mlsanity.com/paper/2505.11703/) (2025)
- [RulerNet: Learning Perspective-Invariant Ruler Representations for Robust Image Scale Estimation](https://mlsanity.com/paper/2507.07077/) (2025)
- [Synthetic Simplicity: Unveiling Bias in Medical Data Augmentation](https://mlsanity.com/paper/2407.21674/) (2024)
- [Canonical Latent Representations in Conditional Diffusion Models](https://mlsanity.com/paper/2506.09955/) (2025)
- [GenView++: Unifying Adaptive Generative Augmentation and Quality-Driven Supervision for Contrastive Representation Learning](https://mlsanity.com/paper/2509.23770/) (2025)
- [Diffusion-Classifier Synergy: Reward-Aligned Learning via Mutual Boosting Loop for FSCIL](https://mlsanity.com/paper/2510.03608/) (2025)
- [Decomposing Subject-Driven Image Generation via Intermediate Structural Prediction](https://mlsanity.com/paper/2605.20807/) (2026)
- [Steering Diffusion Models via Class-Contrastive Influence for Few-Shot Medical Classification](https://mlsanity.com/paper/2607.12464/) (2026)
- [Layering Virtual Try-On](https://mlsanity.com/paper/2607.22924/) (2026)
- [CLARE: Scalable Class-Incremental Continual Learning via a Sparsity-Based Framework](https://mlsanity.com/paper/2609.17026/) (2026)

## Abstract (arXiv)

The acquisition of large-scale, high-quality data is a resource-intensive and time-consuming endeavor. Compared to conventional Data Augmentation (DA) techniques (e.g. cropping and rotation), exploiting prevailing diffusion models for data generation has received scant attention in classification tasks. Existing generative DA methods either inadequately bridge the domain gap between real-world and synthesized images, or inherently suffer from a lack of diversity. To solve these issues, this paper proposes a new classification-oriented framework DreamDA, which enables data synthesis and label generation by way of diffusion models. DreamDA generates diverse samples that adhere to the original data distribution by considering training images in the original data as seeds and perturbing their reverse diffusion process. In addition, since the labels of the generated data may not align with the labels of their corresponding seed images, we introduce a self-training paradigm for generating pseudo labels and training classifiers using the synthesized data. Extensive experiments across four tasks and five datasets demonstrate consistent improvements over strong baselines, revealing the efficacy of DreamDA in synthesizing high-quality and diverse images with accurate labels. Our code will be available at https://github.com/yunxiangfu2001/DreamDA.