# Self-supervised Dataset Distillation: A Good Compression Is All You Need

arXiv: https://arxiv.org/abs/2404.07976 · HTML: https://mlsanity.com/paper/2404.07976/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 64 references that are in the map.
- First posted on arXiv 11 Apr 2024.

## Builds on (most cited first, 17 of 17)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size](https://mlsanity.com/paper/1602.07360/) (2016)
- [Dataset Distillation: A Comprehensive Review](https://mlsanity.com/paper/2301.07014/) (2023)
- [A Comprehensive Survey of Dataset Distillation](https://mlsanity.com/paper/2301.05603/) (2023)
- [Data-free parameter pruning for Deep Neural Networks](https://mlsanity.com/paper/1507.06149/) (2015)
- [On the Diversity and Realism of Distilled Dataset: An Efficient Dataset Distillation Paradigm](https://mlsanity.com/paper/2312.03526/) (2023)
- [Data Distillation: A Survey](https://mlsanity.com/paper/2301.04272/) (2023)
- [Efficient Dataset Distillation via Minimax Diffusion](https://mlsanity.com/paper/2311.15529/) (2023)
- [Generalized Large-Scale Data Condensation via Various Backbone and Statistical Matching](https://mlsanity.com/paper/2311.17950/) (2023)
- [Dataset Distillation via the Wasserstein Metric](https://mlsanity.com/paper/2311.18531/) (2023)
- [Dataset Distillation via Curriculum Data Synthesis in Large Data Era](https://mlsanity.com/paper/2311.18838/) (2023)
- [Towards Adversarially Robust Dataset Distillation by Curvature Regularization](https://mlsanity.com/paper/2403.10045/) (2024)
- [Dataset Distillation via Adversarial Prediction Matching](https://mlsanity.com/paper/2312.08912/) (2023)
- [DD-RobustBench: An Adversarial Robustness Benchmark for Dataset Distillation](https://mlsanity.com/paper/2403.13322/) (2024)
- [Improve Cross-Architecture Generalization on Dataset Distillation](https://mlsanity.com/paper/2402.13007/) (2024)
- [Distributional Dataset Distillation with Subtask Decomposition](https://mlsanity.com/paper/2403.00999/) (2024)
- [One Category One Prompt: Dataset Distillation using Diffusion Models](https://mlsanity.com/paper/2403.07142/) (2024)

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

- [Elucidating the Design Space of Dataset Condensation](https://mlsanity.com/paper/2404.13733/) (2024)
- [Diversity-Driven Synthesis: Enhancing Dataset Distillation through Directed Weight Adjustment](https://mlsanity.com/paper/2409.17612/) (2024)
- [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)
- [Are Large-scale Soft Labels Necessary for Large-scale Dataset Distillation?](https://mlsanity.com/paper/2410.15919/) (2024)
- [CaO2: Rectifying Inconsistencies in Diffusion-Based Dataset Distillation](https://mlsanity.com/paper/2506.22637/) (2025)
- [Breaking Class Barriers: Efficient Dataset Distillation via Inter-Class Feature Compensator](https://mlsanity.com/paper/2408.06927/) (2024)
- [Training-Free Dataset Pruning for Instance Segmentation](https://mlsanity.com/paper/2503.00828/) (2025)
- [Optimizing Distributional Geometry Alignment with Optimal Transport for Generative Dataset Distillation](https://mlsanity.com/paper/2512.00308/) (2025)
- [MetaDD: Boosting Dataset Distillation with Neural Network Architecture-Invariant Generalization](https://mlsanity.com/paper/2410.05103/) (2024)
- [Learning from Random Subspace Exploration: Generalized Test-Time Augmentation with Self-supervised Distillation](https://mlsanity.com/paper/2507.01347/) (2025)
- [IMS3: Breaking Distributional Aggregation in Diffusion-Based Dataset Distillation](https://mlsanity.com/paper/2603.13960/) (2026)
- [Beyond Dataset Distillation: Lossless Dataset Concentration via Diffusion-Assisted Distribution Alignment](https://mlsanity.com/paper/2603.27987/) (2026)
- [Soft Label Pruning and Quantization for Large-Scale Dataset Distillation](https://mlsanity.com/paper/2604.18135/) (2026)

## Abstract (arXiv)

Dataset distillation aims to compress information from a large-scale original dataset to a new compact dataset while striving to preserve the utmost degree of the original data informational essence. Previous studies have predominantly concentrated on aligning the intermediate statistics between the original and distilled data, such as weight trajectory, features, gradient, BatchNorm, etc. In this work, we consider addressing this task through the new lens of model informativeness in the compression stage on the original dataset pretraining. We observe that with the prior state-of-the-art SRe2L, as model sizes increase, it becomes increasingly challenging for supervised pretrained models to recover learned information during data synthesis, as the channel-wise mean and variance inside the model are flatting and less informative. We further notice that larger variances in BN statistics from self-supervised models enable larger loss signals to update the recovered data by gradients, enjoying more informativeness during synthesis. Building on this observation, we introduce SC-DD, a simple yet effective Self-supervised Compression framework for Dataset Distillation that facilitates diverse information compression and recovery compared to traditional supervised learning schemes, further reaps the potential of large pretrained models with enhanced capabilities. Extensive experiments are conducted on CIFAR-100, Tiny-ImageNet and ImageNet-1K datasets to demonstrate the superiority of our proposed approach. The proposed SC-DD outperforms all previous state-of-the-art supervised dataset distillation methods when employing larger models, such as SRe2L, MTT, TESLA, DC, CAFE, etc., by large margins under the same recovery and post-training budgets. Code is available at https://github.com/VILA-Lab/SRe2L/tree/main/SCDD/.