# Distribution-Aware Data Expansion with Diffusion Models

arXiv: https://arxiv.org/abs/2403.06741 · HTML: https://mlsanity.com/paper/2403.06741/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models.
- Builds on 69 of its 84 references that are in the map.
- First posted on arXiv 11 Mar 2024.

## Placed in

- Diffusion models / [Diffusion foundations and sampling](https://mlsanity.com/diffusion/diffusion-foundations-and-sampling/)

## Builds on (most cited first, 30 of 69)

- [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)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (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)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022)
- [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021)
- [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022)
- [Wide Residual Networks](https://mlsanity.com/paper/1605.07146/) (2016)
- [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://mlsanity.com/paper/1905.04899/) (2019)
- [Large Scale GAN Training for High Fidelity Natural Image Synthesis](https://mlsanity.com/paper/1809.11096/) (2018)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [iCaRL: Incremental Classifier and Representation Learning](https://mlsanity.com/paper/1611.07725/) (2016)
- [RandAugment: Practical automated data augmentation with a reduced search space](https://mlsanity.com/paper/1909.13719/) (2019)
- [Describing Textures in the Wild](https://mlsanity.com/paper/1311.3618/) (2013)
- [Imagen Video: High Definition Video Generation with Diffusion Models](https://mlsanity.com/paper/2210.02303/) (2022)
- [Improved Regularization of Convolutional Neural Networks with Cutout](https://mlsanity.com/paper/1708.04552/) (2017)
- [Image Super-Resolution via Iterative Refinement](https://mlsanity.com/paper/2104.07636/) (2021)
- [RePaint: Inpainting using Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2201.09865/) (2022)
- [Palette: Image-to-Image Diffusion Models](https://mlsanity.com/paper/2111.05826/) (2021)
- [Random Erasing Data Augmentation](https://mlsanity.com/paper/1708.04896/) (2017)
- [Reproducible scaling laws for contrastive language-image learning](https://mlsanity.com/paper/2212.07143/) (2022)
- [Cascaded Diffusion Models for High Fidelity Image Generation](https://mlsanity.com/paper/2106.15282/) (2021)

## Built on by (most cited first, 20 of 20)

- [Human-Guided Image Generation for Expanding Small-Scale Training Image Datasets](https://mlsanity.com/paper/2412.16839/) (2024)
- [ReCon: Region-Controllable Data Augmentation with Rectification and Alignment for Object Detection](https://mlsanity.com/paper/2510.15783/) (2025)
- [BOOD: Boundary-based Out-Of-Distribution Data Generation](https://mlsanity.com/paper/2508.00350/) (2025)
- [Harnessing Synthetic Data from Generative AI for Statistical Inference](https://mlsanity.com/paper/2603.05396/) (2026)
- [Improving Knowledge Distillation Under Unknown Covariate Shift Through Confidence-Guided Data Augmentation](https://mlsanity.com/paper/2506.02294/) (2025)
- [UtilGen: Utility-Centric Generative Data Augmentation with Dual-Level Task Adaptation](https://mlsanity.com/paper/2510.24262/) (2025)
- [DiffSparse: Accelerating Diffusion Transformers with Learned Token Sparsity](https://mlsanity.com/paper/2604.03674/) (2026)
- [Semantic Hierarchical Prompt Tuning for Parameter-Efficient Fine-Tuning](https://mlsanity.com/paper/2412.16956/) (2024)
- [Knowledge-Driven Agentic Scientific Corpus Distillation Framework for Biomedical Large Language Models Training](https://mlsanity.com/paper/2504.19565/) (2025)
- [SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation](https://mlsanity.com/paper/2506.19360/) (2025)
- [Enhancing Small-Scale Dataset Expansion with Triplet-Connection-based Sample Re-Weighting](https://mlsanity.com/paper/2508.07723/) (2025)
- [DiffBench Meets DiffAgent: End-to-End LLM-Driven Diffusion Acceleration Code Generation](https://mlsanity.com/paper/2601.03178/) (2026)
- [Mango-GS: Enhancing Spatio-Temporal Consistency in Dynamic Scenes Reconstruction using Multi-Frame Node-Guided 4D Gaussian Splatting](https://mlsanity.com/paper/2603.11543/) (2026)
- [HiGFA: Hierarchical Guidance for Fine-grained Data Augmentation with Diffusion Models](https://mlsanity.com/paper/2511.12547/) (2025)
- [Invisible Clean-Label Backdoor Attacks for Generative Data Augmentation](https://mlsanity.com/paper/2602.03316/) (2026)
- [Diffusion-Based Data Augmentation for Image Recognition: A Systematic Analysis and Evaluation](https://mlsanity.com/paper/2603.08364/) (2026)
- [DanceDuo: Bridging Human Movement and AI Choreography](https://mlsanity.com/paper/2606.26507/) (2026)
- [Steering Diffusion Models via Class-Contrastive Influence for Few-Shot Medical Classification](https://mlsanity.com/paper/2607.12464/) (2026)
- [Poplar: A Scalable Pipeline for Human-Centric Image Dataset Synthesis](https://mlsanity.com/paper/2608.00440/) (2026)
- [Learning-State-Aware Dynamic Generative Data Augmentation on Small-Scale Datasets](https://mlsanity.com/paper/2608.18907/) (2026)

## Abstract (arXiv)

The scale and quality of a dataset significantly impact the performance of deep models. However, acquiring large-scale annotated datasets is both a costly and time-consuming endeavor. To address this challenge, dataset expansion technologies aim to automatically augment datasets, unlocking the full potential of deep models. Current data expansion techniques include image transformation and image synthesis methods. Transformation-based methods introduce only local variations, leading to limited diversity. In contrast, synthesis-based methods generate entirely new content, greatly enhancing informativeness. However, existing synthesis methods carry the risk of distribution deviations, potentially degrading model performance with out-of-distribution samples. In this paper, we propose DistDiff, a training-free data expansion framework based on the distribution-aware diffusion model. DistDiff constructs hierarchical prototypes to approximate the real data distribution, optimizing latent data points within diffusion models with hierarchical energy guidance. We demonstrate its capability to generate distribution-consistent samples, significantly improving data expansion tasks. DistDiff consistently enhances accuracy across a diverse range of datasets compared to models trained solely on original data. Furthermore, our approach consistently outperforms existing synthesis-based techniques and demonstrates compatibility with widely adopted transformation-based augmentation methods. Additionally, the expanded dataset exhibits robustness across various architectural frameworks. Our code is available at https://github.com/haoweiz23/DistDiff