# Enhancing Dataset Distillation via Non-Critical Region Refinement

arXiv: https://arxiv.org/abs/2503.18267 · HTML: https://mlsanity.com/paper/2503.18267/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 37 references that are in the map.
- First posted on arXiv 24 Mar 2025.

## Builds on (most cited first, 25 of 25)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [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)
- [Wide Residual Networks](https://mlsanity.com/paper/1605.07146/) (2016)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Gradient based sample selection for online continual learning](https://mlsanity.com/paper/1903.08671/) (2019)
- [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)
- [Dataset Distillation](https://mlsanity.com/paper/1811.10959/) (2018)
- [Scaling Up Dataset Distillation to ImageNet-1K with Constant Memory](https://mlsanity.com/paper/2211.10586/) (2022)
- [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)
- [On the Diversity and Realism of Distilled Dataset: An Efficient Dataset Distillation Paradigm](https://mlsanity.com/paper/2312.03526/) (2023)
- [Efficient Dataset Distillation via Minimax Diffusion](https://mlsanity.com/paper/2311.15529/) (2023)
- [M3D: Dataset Condensation by Minimizing Maximum Mean Discrepancy](https://mlsanity.com/paper/2312.15927/) (2023)
- [Delving into Effective Gradient Matching for Dataset Condensation](https://mlsanity.com/paper/2208.00311/) (2022)
- [NAYER: Noisy Layer Data Generation for Efficient and Effective Data-free Knowledge Distillation](https://mlsanity.com/paper/2310.00258/) (2023)
- [Text-Enhanced Data-free Approach for Federated Class-Incremental Learning](https://mlsanity.com/paper/2403.14101/) (2024)
- [ShapeFormer: Shapelet Transformer for Multivariate Time Series Classification](https://mlsanity.com/paper/2405.14608/) (2024)
- [Large-Scale Data-Free Knowledge Distillation for ImageNet via Multi-Resolution Data Generation](https://mlsanity.com/paper/2411.17046/) (2024)

## Built on by (most cited first, 9 of 9)

- [Efficient Multimodal Dataset Distillation via Generative Models](https://mlsanity.com/paper/2509.15472/) (2025)
- [Beyond Random: Automatic Inner-loop Optimization in Dataset Distillation](https://mlsanity.com/paper/2510.04838/) (2025)
- [Hard Labels In! Rethinking the Role of Hard Labels in Mitigating Local Semantic Drift](https://mlsanity.com/paper/2512.15647/) (2025)
- [Condensing Large-Scale Datasets Directly with Minimal Information Loss](https://mlsanity.com/paper/2607.00916/) (2026)
- [Distill Video Datasets into Images](https://mlsanity.com/paper/2512.14621/) (2025)
- [Accelerating Large-Scale Dataset Distillation via Exploration-Exploitation Optimization](https://mlsanity.com/paper/2602.15277/) (2026)
- [Multimodal Dataset Distillation Made Simple by Prototype-Guided Data Synthesis](https://mlsanity.com/paper/2602.19756/) (2026)
- [Memory-efficient Continual Learning with Prototypical Exemplar Condensation](https://mlsanity.com/paper/2603.13804/) (2026)
- [Test-Time Instance-Specific Parameter Composition: A New Paradigm for Adaptive Generative Modeling](https://mlsanity.com/paper/2603.27665/) (2026)

## Abstract (arXiv)

Dataset distillation has become a popular method for compressing large datasets into smaller, more efficient representations while preserving critical information for model training. Data features are broadly categorized into two types: instance-specific features, which capture unique, fine-grained details of individual examples, and class-general features, which represent shared, broad patterns across a class. However, previous approaches often struggle to balance these features-some focus solely on class-general patterns, neglecting finer instance details, while others prioritize instance-specific features, overlooking the shared characteristics essential for class-level understanding. In this paper, we introduce the Non-Critical Region Refinement Dataset Distillation (NRR-DD) method, which preserves instance-specific details and fine-grained regions in synthetic data while enriching non-critical regions with class-general information. This approach enables models to leverage all pixel information, capturing both feature types and enhancing overall performance. Additionally, we present Distance-Based Representative (DBR) knowledge transfer, which eliminates the need for soft labels in training by relying on the distance between synthetic data predictions and one-hot encoded labels. Experimental results show that NRR-DD achieves state-of-the-art performance on both small- and large-scale datasets. Furthermore, by storing only two distances per instance, our method delivers comparable results across various settings. The code is available at https://github.com/tmtuan1307/NRR-DD.