# Generative Dataset Distillation: Balancing Global Structure and Local Details

arXiv: https://arxiv.org/abs/2404.17732 · HTML: https://mlsanity.com/paper/2404.17732/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 42 references that are in the map.
- First posted on arXiv 26 Apr 2024.

## Builds on (most cited first, 30 of 33)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [An Empirical Study of Example Forgetting during Deep Neural Network Learning](https://mlsanity.com/paper/1812.05159/) (2018)
- [Dataset Condensation with Gradient Matching](https://mlsanity.com/paper/2006.05929/) (2020)
- [Dataset Distillation by Matching Training Trajectories](https://mlsanity.com/paper/2203.11932/) (2022)
- [Dynamic Few-Shot Visual Learning without Forgetting](https://mlsanity.com/paper/1804.09458/) (2018)
- [Dataset Condensation with Distribution Matching](https://mlsanity.com/paper/2110.04181/) (2021)
- [Optimizing Millions of Hyperparameters by Implicit Differentiation](https://mlsanity.com/paper/1911.02590/) (2019)
- [Dataset Condensation with Differentiable Siamese Augmentation](https://mlsanity.com/paper/2102.08259/) (2021)
- [Dataset Distillation](https://mlsanity.com/paper/1811.10959/) (2018)
- [Dataset Meta-Learning from Kernel Ridge-Regression](https://mlsanity.com/paper/2011.00050/) (2020)
- [Dataset Distillation with Infinitely Wide Convolutional Networks](https://mlsanity.com/paper/2107.13034/) (2021)
- [Dataset Condensation via Efficient Synthetic-Data Parameterization](https://mlsanity.com/paper/2205.14959/) (2022)
- [Dataset Distillation using Neural Feature Regression](https://mlsanity.com/paper/2206.00719/) (2022)
- [Fair Clustering Through Fairlets](https://mlsanity.com/paper/1802.05733/) (2018)
- [Towards Lossless Dataset Distillation via Difficulty-Aligned Trajectory Matching](https://mlsanity.com/paper/2310.05773/) (2023)
- [A Comprehensive Survey of Dataset Distillation](https://mlsanity.com/paper/2301.05603/) (2023)
- [Soft-Label Dataset Distillation and Text Dataset Distillation](https://mlsanity.com/paper/1910.02551/) (2019)
- [Dataset Condensation with Contrastive Signals](https://mlsanity.com/paper/2202.02916/) (2022)
- [Flexible Dataset Distillation: Learn Labels Instead of Images](https://mlsanity.com/paper/2006.08572/) (2020)
- [DataDAM: Efficient Dataset Distillation with Attention Matching](https://mlsanity.com/paper/2310.00093/) (2023)
- [Pre-Trained Image Encoder for Generalizable Visual Reinforcement Learning](https://mlsanity.com/paper/2212.08860/) (2022)
- [Soft-Label Anonymous Gastric X-ray Image Distillation](https://mlsanity.com/paper/2104.02857/) (2021)
- [Federated Learning via Decentralized Dataset Distillation in Resource-Constrained Edge Environments](https://mlsanity.com/paper/2208.11311/) (2022)
- [DiM: Distilling Dataset into Generative Model](https://mlsanity.com/paper/2303.04707/) (2023)
- [Data-centric AI: Perspectives and Challenges](https://mlsanity.com/paper/2301.04819/) (2023)
- [Delving into Effective Gradient Matching for Dataset Condensation](https://mlsanity.com/paper/2208.00311/) (2022)
- [Compressed Gastric Image Generation Based on Soft-Label Dataset Distillation for Medical Data Sharing](https://mlsanity.com/paper/2209.14635/) (2022)
- [Dataset Distillation Using Parameter Pruning](https://mlsanity.com/paper/2209.14609/) (2022)

## Built on by (most cited first, 18 of 18)

- [The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions](https://mlsanity.com/paper/2502.05673/) (2025)
- [Hyperbolic Dataset Distillation](https://mlsanity.com/paper/2505.24623/) (2025)
- [Generative Dataset Distillation Based on Diffusion Model](https://mlsanity.com/paper/2408.08610/) (2024)
- [Diversity-Driven Generative Dataset Distillation Based on Diffusion Model with Self-Adaptive Memory](https://mlsanity.com/paper/2505.19469/) (2025)
- [Information-Guided Diffusion Sampling for Dataset Distillation](https://mlsanity.com/paper/2507.04619/) (2025)
- [Task-Specific Generative Dataset Distillation with Difficulty-Guided Sampling](https://mlsanity.com/paper/2507.03331/) (2025)
- [Decoupled Audio-Visual Dataset Distillation](https://mlsanity.com/paper/2511.17890/) (2025)
- [Generative Dataset Distillation Based on Self-knowledge Distillation](https://mlsanity.com/paper/2501.04202/) (2025)
- [FD2: A Dedicated Framework for Fine-Grained Dataset Distillation](https://mlsanity.com/paper/2603.25144/) (2026)
- [Learnability-Guided Diffusion for Dataset Distillation](https://mlsanity.com/paper/2604.00519/) (2026)
- [Closed-Form Linear-Probe Dataset Distillation for Pre-trained Vision Models](https://mlsanity.com/paper/2605.07194/) (2026)
- [Latent Video Dataset Distillation](https://mlsanity.com/paper/2504.17132/) (2025)
- [Efficient Video Dataset Distillation via Cluster-Guided Prototype Blending](https://mlsanity.com/paper/2608.03269/) (2026)
- [Secure and Explainable Fraud Detection in Finance via Hierarchical Multi-source Dataset Distillation](https://mlsanity.com/paper/2512.21866/) (2025)
- [Foreground-Aware Dataset Distillation via Dynamic Patch Selection](https://mlsanity.com/paper/2601.02727/) (2026)
- [Difficulty-guided Sampling: Bridging the Target Gap between Dataset Distillation and Downstream Tasks](https://mlsanity.com/paper/2601.10090/) (2026)
- [Soft Label Pruning and Quantization for Large-Scale Dataset Distillation](https://mlsanity.com/paper/2604.18135/) (2026)
- [SAS: Semantic-aware Sampling for Generative Dataset Distillation](https://mlsanity.com/paper/2605.18012/) (2026)

## Abstract (arXiv)

In this paper, we propose a new dataset distillation method that considers balancing global structure and local details when distilling the information from a large dataset into a generative model. Dataset distillation has been proposed to reduce the size of the required dataset when training models. The conventional dataset distillation methods face the problem of long redeployment time and poor cross-architecture performance. Moreover, previous methods focused too much on the high-level semantic attributes between the synthetic dataset and the original dataset while ignoring the local features such as texture and shape. Based on the above understanding, we propose a new method for distilling the original image dataset into a generative model. Our method involves using a conditional generative adversarial network to generate the distilled dataset. Subsequently, we ensure balancing global structure and local details in the distillation process, continuously optimizing the generator for more information-dense dataset generation.