# Dataset Distillation via Vision-Language Category Prototype

arXiv: https://arxiv.org/abs/2506.23580 · HTML: https://mlsanity.com/paper/2506.23580/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 46 references that are in the map.
- First posted on arXiv 30 Jun 2025.

## Builds on (most cited first, 30 of 32)

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [Improved Baselines with Visual Instruction Tuning](https://mlsanity.com/paper/2310.03744/) (2023)
- [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 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)
- [Dataset Distillation using Neural Feature Regression](https://mlsanity.com/paper/2206.00719/) (2022)
- [Squeeze, Recover and Relabel: Dataset Condensation at ImageNet Scale From A New Perspective](https://mlsanity.com/paper/2306.13092/) (2023)
- [Generalizing Dataset Distillation via Deep Generative Prior](https://mlsanity.com/paper/2305.01649/) (2023)
- [Improved Distribution Matching for Dataset Condensation](https://mlsanity.com/paper/2307.09742/) (2023)
- [Privacy for Free: How does Dataset Condensation Help Privacy?](https://mlsanity.com/paper/2206.00240/) (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)
- [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)
- [Remember the Past: Distilling Datasets into Addressable Memories for Neural Networks](https://mlsanity.com/paper/2206.02916/) (2022)
- [DREAM: Efficient Dataset Distillation by Representative Matching](https://mlsanity.com/paper/2302.14416/) (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)
- [Synthesizing Informative Training Samples with GAN](https://mlsanity.com/paper/2204.07513/) (2022)
- [D4M: Dataset Distillation via Disentangled Diffusion Model](https://mlsanity.com/paper/2407.15138/) (2024)
- [Graph Condensation via Receptive Field Distribution Matching](https://mlsanity.com/paper/2206.13697/) (2022)
- [Dataset Distillation with Convexified Implicit Gradients](https://mlsanity.com/paper/2302.06755/) (2023)
- [DiM: Distilling Dataset into Generative Model](https://mlsanity.com/paper/2303.04707/) (2023)
- [A Survey on Dataset Distillation: Approaches, Applications and Future Directions](https://mlsanity.com/paper/2305.01975/) (2023)

## Built on by (most cited first, 19 of 19)

- [ManifoldGD: Training-Free Hierarchical Manifold Guidance for Diffusion-Based Dataset Distillation](https://mlsanity.com/paper/2602.23295/) (2026)
- [EVLF: Early Vision-Language Fusion for Generative Dataset Distillation](https://mlsanity.com/paper/2603.07476/) (2026)
- [Diffusion Models as Dataset Distillation Priors](https://mlsanity.com/paper/2510.17421/) (2025)
- [FD2: A Dedicated Framework for Fine-Grained Dataset Distillation](https://mlsanity.com/paper/2603.25144/) (2026)
- [Multimodal Distribution Matching for Vision-Language Dataset Distillation](https://mlsanity.com/paper/2605.23482/) (2026)
- [EDITS: Enhancing Dataset Distillation with Implicit Textual Semantics](https://mlsanity.com/paper/2509.13858/) (2025)
- [Hard Labels In! Rethinking the Role of Hard Labels in Mitigating Local Semantic Drift](https://mlsanity.com/paper/2512.15647/) (2025)
- [Structural Assessment for Understanding and Guiding Dataset Distillation in Discrete Token Space](https://mlsanity.com/paper/2606.21705/) (2026)
- [Efficient Video Dataset Distillation via Cluster-Guided Prototype Blending](https://mlsanity.com/paper/2608.03269/) (2026)
- [Label-Consistent Dataset Distillation with Detector-Guided Refinement](https://mlsanity.com/paper/2507.13074/) (2025)
- [Multimodal Dataset Distillation Made Simple by Prototype-Guided Data Synthesis](https://mlsanity.com/paper/2602.19756/) (2026)
- [IMS3: Breaking Distributional Aggregation in Diffusion-Based Dataset Distillation](https://mlsanity.com/paper/2603.13960/) (2026)
- [D3S2: Diffusion-Guided Dataset Distillation for Semantic Segmentation](https://mlsanity.com/paper/2605.25022/) (2026)
- [Rethinking Dataset Distillation for Classification: Do Distilled Sets Outperform Coresets?](https://mlsanity.com/paper/2606.18209/) (2026)
- [Dataset Distillation Based on Saliency-Driven Prototype Alignment](https://mlsanity.com/paper/2607.25318/) (2026)
- [DeCO: Discriminative Evidence Composition for Fine-Grained Dataset Distillation](https://mlsanity.com/paper/2608.25480/) (2026)
- [PSM: Dataset Distillation Based on Precise Statistical Matching by Difficulty](https://mlsanity.com/paper/2609.34299/) (2026)
- [MGPO: Manifold-Guided Diffusion Alignment for Task-Aware Dataset Distillation](https://mlsanity.com/paper/2610.05252/) (2026)
- [ProMoDD: Dataset Distillation for 3D Human Motion via Prototype-Conditioned Diffusion Models](https://mlsanity.com/paper/2610.16134/) (2026)

## Abstract (arXiv)

Dataset distillation (DD) condenses large datasets into compact yet informative substitutes, preserving performance comparable to the original dataset while reducing storage, transmission costs, and computational consumption. However, previous DD methods mainly focus on distilling information from images, often overlooking the semantic information inherent in the data. The disregard for context hinders the model's generalization ability, particularly in tasks involving complex datasets, which may result in illogical outputs or the omission of critical objects. In this study, we integrate vision-language methods into DD by introducing text prototypes to distill language information and collaboratively synthesize data with image prototypes, thereby enhancing dataset distillation performance. Notably, the text prototypes utilized in this study are derived from descriptive text information generated by an open-source large language model. This framework demonstrates broad applicability across datasets without pre-existing text descriptions, expanding the potential of dataset distillation beyond traditional image-based approaches. Compared to other methods, the proposed approach generates logically coherent images containing target objects, achieving state-of-the-art validation performance and demonstrating robust generalization. Source code and generated data are available in https://github.com/zou-yawen/Dataset-Distillation-via-Vision-Language-Category-Prototype/