# Diversity-Driven Synthesis: Enhancing Dataset Distillation through Directed Weight Adjustment

arXiv: https://arxiv.org/abs/2409.17612 · HTML: https://mlsanity.com/paper/2409.17612/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- Builds on 44 of its 55 references that are in the map.
- First posted on arXiv 26 Sep 2024.

## Builds on (most cited first, 30 of 44)

- [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)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Training data-efficient image transformers & distillation through attention](https://mlsanity.com/paper/2012.12877/) (2020)
- [Training Compute-Optimal Large Language Models](https://mlsanity.com/paper/2203.15556/) (2022)
- [The Pile: An 800GB Dataset of Diverse Text for Language Modeling](https://mlsanity.com/paper/2101.00027/) (2020)
- [Knowledge Distillation: A Survey](https://mlsanity.com/paper/2006.05525/) (2020)
- [ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design](https://mlsanity.com/paper/1807.11164/) (2018)
- [An Empirical Study of Example Forgetting during Deep Neural Network Learning](https://mlsanity.com/paper/1812.05159/) (2018)
- [Deep Learning on a Data Diet: Finding Important Examples Early in Training](https://mlsanity.com/paper/2107.07075/) (2021)
- [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)
- [Dreaming to Distill: Data-free Knowledge Transfer via DeepInversion](https://mlsanity.com/paper/1912.08795/) (2019)
- [Dataset Condensation with Distribution Matching](https://mlsanity.com/paper/2110.04181/) (2021)
- [Dataset Condensation with Differentiable Siamese Augmentation](https://mlsanity.com/paper/2102.08259/) (2021)
- [Dataset Meta-Learning from Kernel Ridge-Regression](https://mlsanity.com/paper/2011.00050/) (2020)
- [Dataset Condensation via Efficient Synthetic-Data Parameterization](https://mlsanity.com/paper/2205.14959/) (2022)
- [Scaling Up Dataset Distillation to ImageNet-1K with Constant Memory](https://mlsanity.com/paper/2211.10586/) (2022)
- [D4: Improving LLM Pretraining via Document De-Duplication and Diversification](https://mlsanity.com/paper/2308.12284/) (2023)
- [Minimizing the Accumulated Trajectory Error to Improve Dataset Distillation](https://mlsanity.com/paper/2211.11004/) (2022)
- [Squeeze, Recover and Relabel: Dataset Condensation at ImageNet Scale From A New Perspective](https://mlsanity.com/paper/2306.13092/) (2023)
- [Improved Distribution Matching for Dataset Condensation](https://mlsanity.com/paper/2307.09742/) (2023)
- [Efficient Multimodal Learning from Data-centric Perspective](https://mlsanity.com/paper/2402.11530/) (2024)
- [Coverage-centric Coreset Selection for High Pruning Rates](https://mlsanity.com/paper/2210.15809/) (2022)
- [A Comprehensive Survey of Dataset Distillation](https://mlsanity.com/paper/2301.05603/) (2023)
- [Efficient Dataset Distillation Using Random Feature Approximation](https://mlsanity.com/paper/2210.12067/) (2022)
- [On the Diversity and Realism of Distilled Dataset: An Efficient Dataset Distillation Paradigm](https://mlsanity.com/paper/2312.03526/) (2023)

## Built on by (most cited first, 30 of 33)

- [The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions](https://mlsanity.com/paper/2502.05673/) (2025)
- [DELT: A Simple Diversity-driven EarlyLate Training for Dataset Distillation](https://mlsanity.com/paper/2411.19946/) (2024)
- [Emphasizing Discriminative Features for Dataset Distillation in Complex Scenarios](https://mlsanity.com/paper/2410.17193/) (2024)
- [Taming Diffusion for Dataset Distillation with High Representativeness](https://mlsanity.com/paper/2505.18399/) (2025)
- [PosterGen: Aesthetic-Aware Multi-Modal Paper-to-Poster Generation via Multi-Agent LLMs](https://mlsanity.com/paper/2508.17188/) (2025)
- [DD-Ranking: Rethinking the Evaluation of Dataset Distillation](https://mlsanity.com/paper/2505.13300/) (2025)
- [Beyond Modality Collapse: Representations Blending for Multimodal Dataset Distillation](https://mlsanity.com/paper/2505.14705/) (2025)
- [Efficient Multimodal Dataset Distillation via Generative Models](https://mlsanity.com/paper/2509.15472/) (2025)
- [Unifying Dataset Pruning and Distillation for Efficient Large-scale Compression](https://mlsanity.com/paper/2502.06434/) (2025)
- [Optimizing Distributional Geometry Alignment with Optimal Transport for Generative Dataset Distillation](https://mlsanity.com/paper/2512.00308/) (2025)
- [Going Beyond Feature Similarity: Effective Dataset Distillation based on Class-Aware Conditional Mutual Information](https://mlsanity.com/paper/2412.09945/) (2024)
- [Understanding Dataset Distillation via Spectral Filtering](https://mlsanity.com/paper/2503.01212/) (2025)
- [Enhancing Diffusion-based Dataset Distillation via Adversary-Guided Curriculum Sampling](https://mlsanity.com/paper/2508.01264/) (2025)
- [Research Superalignment Should Advance Now with Alternating Competence and Conformity Optimization](https://mlsanity.com/paper/2503.07660/) (2025)
- [SCORE: Soft Label Compression-Centric Dataset Condensation via Coding Rate Optimization](https://mlsanity.com/paper/2503.13935/) (2025)
- [Temporal Saliency-Guided Distillation: A Scalable Framework for Distilling Video Datasets](https://mlsanity.com/paper/2505.20694/) (2025)
- [Instance Data Condensation for Image Super-Resolution](https://mlsanity.com/paper/2505.21099/) (2025)
- [High-Energy Concentration for Federated Learning in Frequency Domain](https://mlsanity.com/paper/2509.12630/) (2025)
- [EDITS: Enhancing Dataset Distillation with Implicit Textual Semantics](https://mlsanity.com/paper/2509.13858/) (2025)
- [Rectified Decoupled Dataset Distillation: A Closer Look for Fair and Comprehensive Evaluation](https://mlsanity.com/paper/2509.19743/) (2025)
- [DiRe: Diversity-promoting Regularization for Dataset Condensation](https://mlsanity.com/paper/2512.13083/) (2025)
- [Multimodal Dataset Distillation via Phased Teacher Models](https://mlsanity.com/paper/2603.25388/) (2026)
- [Rethinking Dataset Distillation: Hard Truths about Soft Labels](https://mlsanity.com/paper/2604.18811/) (2026)
- [Condensing Large-Scale Datasets Directly with Minimal Information Loss](https://mlsanity.com/paper/2607.00916/) (2026)
- [Not All Instances Are Equally Valuable: Towards Influence-Weighted Dataset Distillation](https://mlsanity.com/paper/2510.27253/) (2025)
- [Distill Video Datasets into Images](https://mlsanity.com/paper/2512.14621/) (2025)
- [Foreground-Aware Dataset Distillation via Dynamic Patch Selection](https://mlsanity.com/paper/2601.02727/) (2026)
- [Accelerating Large-Scale Dataset Distillation via Exploration-Exploitation Optimization](https://mlsanity.com/paper/2602.15277/) (2026)
- [Memory-efficient Continual Learning with Prototypical Exemplar Condensation](https://mlsanity.com/paper/2603.13804/) (2026)
- [InkDrop: Invisible Backdoor Attacks Against Dataset Condensation](https://mlsanity.com/paper/2603.28092/) (2026)

## Abstract (arXiv)

The sharp increase in data-related expenses has motivated research into condensing datasets while retaining the most informative features. Dataset distillation has thus recently come to the fore. This paradigm generates synthetic datasets that are representative enough to replace the original dataset in training a neural network. To avoid redundancy in these synthetic datasets, it is crucial that each element contains unique features and remains diverse from others during the synthesis stage. In this paper, we provide a thorough theoretical and empirical analysis of diversity within synthesized datasets. We argue that enhancing diversity can improve the parallelizable yet isolated synthesizing approach. Specifically, we introduce a novel method that employs dynamic and directed weight adjustment techniques to modulate the synthesis process, thereby maximizing the representativeness and diversity of each synthetic instance. Our method ensures that each batch of synthetic data mirrors the characteristics of a large, varying subset of the original dataset. Extensive experiments across multiple datasets, including CIFAR, Tiny-ImageNet, and ImageNet-1K, demonstrate the superior performance of our method, highlighting its effectiveness in producing diverse and representative synthetic datasets with minimal computational expense. Our code is available at https://github.com/AngusDujw/Diversity-Driven-Synthesis.https://github.com/AngusDujw/Diversity-Driven-Synthesis.