# SYNAuG: Exploiting Synthetic Data for Data Imbalance Problems

arXiv: https://arxiv.org/abs/2308.00994 · HTML: https://mlsanity.com/paper/2308.00994/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 79 of its 97 references that are in the map.
- First posted on arXiv 2 Aug 2023.

## Builds on (most cited first, 30 of 79)

- [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)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [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)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [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)
- [DreamFusion: Text-to-3D using 2D Diffusion](https://mlsanity.com/paper/2209.14988/) (2022)
- [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://mlsanity.com/paper/1905.04899/) (2019)
- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [Shortcut Learning in Deep Neural Networks](https://mlsanity.com/paper/2004.07780/) (2020)
- [Invariant Risk Minimization](https://mlsanity.com/paper/1907.02893/) (2019)
- [Contrastive Multiview Coding](https://mlsanity.com/paper/1906.05849/) (2019)
- [Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization](https://mlsanity.com/paper/1911.08731/) (2019)
- [Class-Balanced Loss Based on Effective Number of Samples](https://mlsanity.com/paper/1901.05555/) (2019)
- [FlowNet: Learning Optical Flow with Convolutional Networks](https://mlsanity.com/paper/1504.06852/) (2015)
- [Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss](https://mlsanity.com/paper/1906.07413/) (2019)
- [A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation](https://mlsanity.com/paper/1512.02134/) (2015)
- [A Convergence Theory for Deep Learning via Over-Parameterization](https://mlsanity.com/paper/1811.03962/) (2018)
- [Certifying and removing disparate impact](https://mlsanity.com/paper/1412.3756/) (2014)
- [Large-Scale Long-Tailed Recognition in an Open World](https://mlsanity.com/paper/1904.05160/) (2019)
- [Fantasia3D: Disentangling Geometry and Appearance for High-quality Text-to-3D Content Creation](https://mlsanity.com/paper/2303.13873/) (2023)
- [Just Train Twice: Improving Group Robustness without Training Group Information](https://mlsanity.com/paper/2107.09044/) (2021)
- [Age Progression/Regression by Conditional Adversarial Autoencoder](https://mlsanity.com/paper/1702.08423/) (2017)
- [Deep Long-Tailed Learning: A Survey](https://mlsanity.com/paper/2110.04596/) (2021)
- [Balanced Meta-Softmax for Long-Tailed Visual Recognition](https://mlsanity.com/paper/2007.10740/) (2020)
- [Last Layer Re-Training is Sufficient for Robustness to Spurious Correlations](https://mlsanity.com/paper/2204.02937/) (2022)
- [Synthetic Data from Diffusion Models Improves ImageNet Classification](https://mlsanity.com/paper/2304.08466/) (2023)
- [Is synthetic data from generative models ready for image recognition?](https://mlsanity.com/paper/2210.07574/) (2022)

## Built on by (most cited first, 13 of 13)

- [SGD-Mix: Enhancing Domain-Specific Image Classification with Label-Preserving Data Augmentation](https://mlsanity.com/paper/2505.11813/) (2025)
- [Synthetic Data Augmentation using Pre-trained Diffusion Models for Long-tailed Food Image Classification](https://mlsanity.com/paper/2506.01368/) (2025)
- [Training on Synthetic Data Beats Real Data in Multimodal Relation Extraction](https://mlsanity.com/paper/2312.03025/) (2023)
- [Reflections and New Directions for Human-Centered Large Language Models](https://mlsanity.com/paper/2605.06901/) (2026)
- [Paint Outside the Box: Synthesizing and Selecting Training Data for Visual Grounding](https://mlsanity.com/paper/2412.00684/) (2024)
- [T2ID-CAS: Diffusion Model and Class Aware Sampling to Mitigate Class Imbalance in Neck Ultrasound Anatomical Landmark Detection](https://mlsanity.com/paper/2504.21231/) (2025)
- [LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance](https://mlsanity.com/paper/2505.11703/) (2025)
- [Synthetic Generation and Latent Projection Denoising of Rim Lesions in Multiple Sclerosis](https://mlsanity.com/paper/2505.23353/) (2025)
- [CytoDiff: AI-Driven Cytomorphology Image Synthesis for Medical Diagnostics](https://mlsanity.com/paper/2507.05063/) (2025)
- [Towards High Supervised Learning Utility Training Data Generation: Data Pruning and Column Reordering](https://mlsanity.com/paper/2507.10088/) (2025)
- [On Privacy in Data-Space Tabular Diffusion Models: Influential Factors, Attacker Knowledge, and Metrics](https://mlsanity.com/paper/2605.06835/) (2026)
- [Gen2Balance: Generative Balancing for Long-Tailed Video Action Recognition](https://mlsanity.com/paper/2606.22416/) (2026)
- [Domain generalization and synthetic data in object detection: the enabler, the probe, and the gap](https://mlsanity.com/paper/2610.00030/) (2026)

## Abstract (arXiv)

Data imbalance in training data often leads to biased predictions from trained models, which in turn causes ethical and social issues. A straightforward solution is to carefully curate training data, but given the enormous scale of modern neural networks, this is prohibitively labor-intensive and thus impractical. Inspired by recent developments in generative models, this paper explores the potential of synthetic data to address the data imbalance problem. To be specific, our method, dubbed SYNAuG, leverages synthetic data to equalize the unbalanced distribution of training data. Our experiments demonstrate that, although a domain gap between real and synthetic data exists, training with SYNAuG followed by fine-tuning with a few real samples allows to achieve impressive performance on diverse tasks with different data imbalance issues, surpassing existing task-specific methods for the same purpose.