# Advances in Diffusion Models for Image Data Augmentation: A Review of Methods, Models, Evaluation Metrics and Future Research Directions

arXiv: https://arxiv.org/abs/2407.04103 · HTML: https://mlsanity.com/paper/2407.04103/

## Key facts

- Cited by 14 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 3 of its 274 references that are in the map.
- First posted on arXiv 4 Jul 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, 3 of 3)

- [Advancing Fine-Grained Classification by Structure and Subject Preserving Augmentation](https://mlsanity.com/paper/2406.14551/) (2024)
- [SemanticDraw: Towards Real-Time Interactive Content Creation from Image Diffusion Models](https://mlsanity.com/paper/2403.09055/) (2024)
- [GUIDE: Guidance-based Incremental Learning with Diffusion Models](https://mlsanity.com/paper/2403.03938/) (2024)

## Built on by (most cited first, 14 of 14)

- [TRIFFID: Autonomous Robotic Aid For Increasing First Responders Efficiency](https://mlsanity.com/paper/2502.09379/) (2025)
- [DILLEMA: Diffusion and Large Language Models for Multi-Modal Augmentation](https://mlsanity.com/paper/2502.04378/) (2025)
- [Rendering-Refined Stable Diffusion for Privacy Compliant Synthetic Data](https://mlsanity.com/paper/2412.06248/) (2024)
- [SmartSpatial: Enhancing the 3D Spatial Arrangement Capabilities of Stable Diffusion Models and Introducing a Novel 3D Spatial Evaluation Framework](https://mlsanity.com/paper/2501.01998/) (2025)
- [Enhancing Zero-Shot Pedestrian Attribute Recognition with Synthetic Data Generation: A Comparative Study with Image-To-Image Diffusion Models](https://mlsanity.com/paper/2509.02161/) (2025)
- [PropFly: Learning to Propagate via On-the-Fly Supervision from Pre-trained Video Diffusion Models](https://mlsanity.com/paper/2602.20583/) (2026)
- [A framework for river connectivity classification using temporal image processing and attention based neural networks](https://mlsanity.com/paper/2502.00474/) (2025)
- [PAT++: a cautionary tale about generative visual augmentation for Object Re-identification](https://mlsanity.com/paper/2507.15888/) (2025)
- [BrainPath: A Biologically-Informed AI Framework for Individualized Aging Brain Generation](https://mlsanity.com/paper/2508.16667/) (2025)
- [NoiseCutMix: A Novel Data Augmentation Approach by Mixing Estimated Noise in Diffusion Models](https://mlsanity.com/paper/2509.00378/) (2025)
- [A Hybrid Approach for Closing the Sim2real Appearance Gap in Game Engine Synthetic Datasets](https://mlsanity.com/paper/2605.02291/) (2026)
- [Search-based Testing of Vision Language Models for In-Car Scene Understanding](https://mlsanity.com/paper/2607.02300/) (2026)
- [Breaking the Synthetic-Real Domain Shortcut for Training-Free Generative Replay-based Class Incremental Learning](https://mlsanity.com/paper/2607.22994/) (2026)
- [Perturbation-Aware Diffusion-Guided Hybrid Segmentation for Robust and Annotation-Efficient Plant Stress Phenotyping](https://mlsanity.com/paper/2607.23680/) (2026)

## Abstract (arXiv)

Image data augmentation constitutes a critical methodology in modern computer vision tasks, since it can facilitate towards enhancing the diversity and quality of training datasets; thereby, improving the performance and robustness of machine learning models in downstream tasks. In parallel, augmentation approaches can also be used for editing/modifying a given image in a context- and semantics-aware way. Diffusion Models (DMs), which comprise one of the most recent and highly promising classes of methods in the field of generative Artificial Intelligence (AI), have emerged as a powerful tool for image data augmentation, capable of generating realistic and diverse images by learning the underlying data distribution. The current study realizes a systematic, comprehensive and in-depth review of DM-based approaches for image augmentation, covering a wide range of strategies, tasks and applications. In particular, a comprehensive analysis of the fundamental principles, model architectures and training strategies of DMs is initially performed. Subsequently, a taxonomy of the relevant image augmentation methods is introduced, focusing on techniques regarding semantic manipulation, personalization and adaptation, and application-specific augmentation tasks. Then, performance assessment methodologies and respective evaluation metrics are analyzed. Finally, current challenges and future research directions in the field are discussed.