arxiv: 2605.03877 · v1 · submitted 2026-05-05 · 💻 cs.CV · cs.AI

Recognition: unknown

DMGD: Train-Free Dataset Distillation with Semantic-Distribution Matching in Diffusion Models

Qichao Wang , Yunhong Lu , Hengyuan Cao , Junyi Zhang , Min Zhang

Authors on Pith no claims yet

Pith reviewed 2026-05-07 17:35 UTC · model grok-4.3

classification 💻 cs.CV cs.AI

keywords dataset distillationdiffusion modelssemantic matchingoptimal transporttrain-freesynthetic dataImageNetguidance mechanism

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The pith

A training-free diffusion method creates small synthetic datasets that outperform fine-tuned distillation approaches on ImageNet subsets.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents a Dual Matching Guided Diffusion framework for dataset distillation that generates useful synthetic images directly from diffusion models without any training or fine-tuning steps. It achieves this by matching semantics through conditional likelihood optimization, aligning distributions with optimal transport, and using dynamic guidance to keep the outputs varied yet on-topic. Efficient matching strategies reduce the computational cost of these alignments. If the approach works as described, it removes the need for auxiliary classifiers or post-training adjustments that previous diffusion-based distillation methods required. This leads to higher accuracy when the synthetic data is used to train downstream models on standard ImageNet variants.

Core claim

The authors establish the DMGD framework for training-free dataset distillation. Semantic Matching occurs via conditional likelihood optimization that eliminates auxiliary classifiers. A dynamic guidance mechanism improves sample diversity while preserving alignment. An optimal transport based Distribution Matching step further aligns the synthetic data with the target distribution structure. Two efficiency strategies, Distribution Approximate Matching and Greedy Progressive Matching, allow the full process to run with low overhead. Experiments show the resulting synthetic sets improve downstream accuracy over prior methods that require fine-tuning.

What carries the argument

The DMGD framework, which performs semantic matching through conditional likelihood optimization, dynamic guidance for diversity, and optimal transport distribution matching together with approximate and progressive efficiency strategies.

If this is right

Synthetic datasets produced without fine-tuning can yield higher downstream classification accuracy than those from methods that include fine-tuning stages.
Optimal transport distribution matching preserves structural properties of the original large dataset in the much smaller synthetic version.
Dynamic guidance during diffusion sampling can maintain semantic correctness while increasing the variety of generated samples.
Approximate and progressive matching strategies allow distribution alignment to occur with only modest extra computation.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same dual-matching idea could be tested on other generative backbones such as GANs or flow models to see if training-free distillation generalizes beyond diffusion.
If the matching mechanisms hold, similar techniques might reduce the data requirements for training large vision models in resource-constrained settings.
The approach suggests that diffusion models already encode enough dataset statistics to support distillation once the right guidance signals are supplied.
Extending the method to video or multimodal datasets would test whether the semantic and distribution matching components remain effective outside static images.

Load-bearing premise

Semantic matching via conditional likelihood optimization combined with optimal transport distribution matching and dynamic guidance can produce high-quality diverse synthetic data that supports strong downstream model performance without fine-tuning or auxiliary classifiers.

What would settle it

Generate the synthetic dataset on ImageNet-1K using the described DMGD procedure, train a standard classifier on it, and check whether accuracy exceeds the reported SOTA fine-tuned baselines by roughly 2.4 percent.

Figures

Figures reproduced from arXiv: 2605.03877 by Hengyuan Cao, Junyi Zhang, Min Zhang, Qichao Wang, Yunhong Lu.

**Figure 1.** Figure 1: A comparison of different diffusion-based paradigms view at source ↗

**Figure 2.** Figure 2: Framework of our DMGD method. Our method establishes two guidance modules during the sampling process: semantic matching and distribution matching. In semantic matching, we propose a dynamic soft label mechanism to unlock the potential of diffusion models for diversified generation while ensuring semantic alignment. In distribution matching, we optimize optimal transport computation through distribution ap… view at source ↗

**Figure 3.** Figure 3: Evaluation results: (a-b) Evaluation of our method’s performance across different architectures and higher IPC settings: Results view at source ↗

**Figure 4.** Figure 4: Generated Samples Visualization: the visual comparison of Golden Retriever in ImageNet-WOOF, we present the generated samples from different methods under the IPC-10 setting. The method names are marked at the left of each row. Method Cov.↑ OTDD↓ Diversity↑ FID↓ DiT [48] 25.4 142.2 70.1 48.6 Minimax [22] 28.5 88.5 72.9 49.2 Ours 30.7 66.4 74.4 48.8 view at source ↗

**Figure 1.** Figure 1: Intuitive demonstration of the dynamic semantic match view at source ↗

**Figure 3.** Figure 3: Distribution Visualization: Visualization results of sample distributions for surrogate datasets generated by different methods and the original dataset: top row corresponds to ImageNet-Woof under IPC-100 setting, bottom row corresponds to ImageNet-Nette under IPC-50 setting. tiveness, reflected by marginally reduced precision. However, the substantially improved recall demonstrates our method’s enhanced … view at source ↗

**Figure 4.** Figure 4: OT Distance Visualization: We systematically recorded the final optimal transport (OT) distance loss for each sample during progressive distillation. A randomly selected category from ImageNet-Woof was visualized to illustrate the results. matching with diversity-enhanced semantic matching (Distribution Matching+Semantic Matching). Each data point represents the final OT distance loss of an individual sa… view at source ↗

**Figure 5.** Figure 5: Generated samples are from our proposed DMGD method for the ImageNet-Woof dataset. We present the randomly selected view at source ↗

**Figure 6.** Figure 6: Generated samples are from our proposed DMGD method for the ImageNet-Nette dataset. We present the randomly selected view at source ↗

read the original abstract

Dataset distillation enables efficient training by distilling the information of large-scale datasets into significantly smaller synthetic datasets. Diffusion based paradigms have emerged in recent years, offering novel perspectives for dataset distillation. However, they typically necessitate additional fine-tuning stages, and effective guidance mechanisms remain underexplored. To address these limitations, we rethink diffusion based dataset distillation and propose a Dual Matching Guided Diffusion (DMGD) framework, centered on efficient training-free guidance. We first establish Semantic Matching via conditional likelihood optimization, eliminating the need for auxiliary classifiers. Furthermore, we propose a dynamic guidance mechanism that enhances the diversity of synthetic data while maintaining semantic alignment. Simultaneously, we introduce an optimal transport (OT) based Distribution Matching approach to further align with the target distribution structure. To ensure efficiency, we develop two enhanced strategies for diffusion based framework: Distribution Approximate Matching and Greedy Progressive Matching. These strategies enable effective distribution matching guidance with minimal computational overhead. Experimental results on ImageNet-Woof, ImageNet-Nette, and ImageNet-1K demonstrate that our training-free approach achieves significant improvements, outperforming state-of-the-art (SOTA) methods requiring additional fine-tuning by average accuracy gains of 2.1%, 5.4%, and 2.4%, respectively.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

DMGD gives a train-free diffusion route to dataset distillation via semantic likelihood matching plus OT distribution alignment, with reported gains over fine-tuned priors on ImageNet subsets.

read the letter

The main point is that this paper removes the fine-tuning stage from diffusion-based dataset distillation by combining conditional-likelihood semantic matching, dynamic guidance for diversity, and optimal transport distribution matching, plus two lightweight efficiency tricks. It reports downstream accuracy lifts of 2.1%, 5.4%, and 2.4% on ImageNet-Woof, Nette, and 1K over prior SOTA that still need extra tuning steps. That combination is the actual novelty; earlier diffusion distillation work leaned on post-hoc fine-tuning, and this one tries to bake the alignment into the generation process itself. The efficiency strategies (approximate matching and greedy progressive matching) are presented as practical ways to keep the overhead manageable without auxiliary classifiers. The experiments supply concrete numbers on standard subsets, which is useful evidence that the guidance scheme can produce usable synthetic data. The approach looks internally coherent: no obvious circular definitions or hidden dependence on training. The soft spots are modest rather than fatal. The gains sit in the low single digits, so they matter for efficiency work but are not dramatic; without seeing variance across seeds or fuller ablations that isolate each matching term, it is hard to judge robustness. The tests stay on three ImageNet subsets, leaving open questions about scaling to noisier or more diverse data. Citation coverage of diffusion and OT literature is standard and not padded. This is for people already working on dataset distillation or efficient vision training who want concrete train-free options. A reader focused on reducing compute for synthetic data generation will find the guidance mechanisms worth examining. It has enough new machinery and empirical backing to merit a serious referee, even if the revision bar includes more controls and larger-scale checks. I would send it to peer review.

Referee Report

2 major / 3 minor

Summary. The manuscript proposes DMGD, a training-free dataset distillation framework for diffusion models. It introduces semantic matching through conditional likelihood optimization (no auxiliary classifier), a dynamic guidance mechanism to improve diversity while preserving alignment, and an OT-based distribution matching step. Two efficiency strategies (Distribution Approximate Matching and Greedy Progressive Matching) are added to keep overhead low. Experiments on ImageNet-Woof, ImageNet-Nette, and ImageNet-1K report average accuracy gains of 2.1%, 5.4%, and 2.4% over fine-tuned SOTA baselines.

Significance. If the empirical claims hold, the result would be significant: it removes the fine-tuning stage and auxiliary-classifier requirement that have been standard in recent diffusion-based distillation work, while still delivering measurable downstream gains. The combination of likelihood-based semantic guidance with OT distribution alignment and the two lightweight matching approximations constitutes a coherent, self-contained pipeline that could simplify large-scale synthetic-data generation.

major comments (2)

[§4] §4 (Method), the conditional-likelihood formulation for semantic matching: the paper states that this step eliminates auxiliary classifiers, yet the precise objective (e.g., the form of the likelihood term and how it is optimized inside the diffusion sampling loop) is not written as an equation or algorithm; without it, it is impossible to confirm that the procedure is truly classifier-free and does not implicitly rely on pre-trained embeddings that function equivalently.
[§5] §5 (Experiments), Tables 1–3: the reported accuracy improvements (2.1–5.4 %) are given as point estimates without error bars, number of random seeds, or statistical significance tests. Because the central claim is that DMGD outperforms fine-tuned SOTA, the absence of these controls makes it impossible to judge whether the gains are robust or could be explained by variance in the downstream training.

minor comments (3)

[Abstract, §3.2] The abstract and §3.2 mention “dynamic guidance” but never define the schedule or the hyper-parameter that trades off diversity versus alignment; a short equation or pseudocode would clarify the mechanism.
[§4.3] Notation for the OT cost matrix and the approximate matching strategies is introduced without a reference to the specific OT solver or the approximation error bound; adding one sentence and a citation would improve reproducibility.
[Figure 2] Figure 2 (qualitative samples) lacks a side-by-side comparison with the strongest baseline at the same IPC; this would help readers visually assess the claimed diversity improvement.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback and the recommendation for minor revision. The comments help improve the clarity of the method and the robustness of the empirical claims. We address each major comment below and commit to the corresponding revisions.

read point-by-point responses

Referee: [§4] §4 (Method), the conditional-likelihood formulation for semantic matching: the paper states that this step eliminates auxiliary classifiers, yet the precise objective (e.g., the form of the likelihood term and how it is optimized inside the diffusion sampling loop) is not written as an equation or algorithm; without it, it is impossible to confirm that the procedure is truly classifier-free and does not implicitly rely on pre-trained embeddings that function equivalently.

Authors: We appreciate the referee's observation. Section 4 describes semantic matching through conditional likelihood optimization to remove the need for auxiliary classifiers, but we agree that an explicit equation and algorithmic presentation are required for full verification and reproducibility. In the revised manuscript we will introduce a dedicated equation for the likelihood objective and add pseudocode (as a new algorithm) that details its optimization inside the diffusion sampling loop, explicitly showing that the procedure remains classifier-free and operates directly on the diffusion model's conditional likelihood without relying on equivalent pre-trained embeddings. revision: yes
Referee: [§5] §5 (Experiments), Tables 1–3: the reported accuracy improvements (2.1–5.4 %) are given as point estimates without error bars, number of random seeds, or statistical significance tests. Because the central claim is that DMGD outperforms fine-tuned SOTA, the absence of these controls makes it impossible to judge whether the gains are robust or could be explained by variance in the downstream training.

Authors: The referee correctly notes that the current tables report only point estimates. While the experiments followed fixed protocols, variability across random seeds and formal significance testing were not included. We will rerun the evaluations on ImageNet-Woof, ImageNet-Nette, and ImageNet-1K using at least five random seeds, update Tables 1–3 to display mean accuracy together with standard deviations, and add paired statistical significance tests against the fine-tuned baselines. These additions will appear in the revised manuscript and will strengthen the evidence for the reported gains. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper introduces a DMGD framework for training-free dataset distillation via semantic matching through conditional likelihood optimization, dynamic guidance for diversity, and OT-based distribution matching, plus two efficiency strategies (Distribution Approximate Matching and Greedy Progressive Matching). No equations, derivations, or first-principles results are presented that reduce any claim to its own inputs by construction. The central performance claims rest on empirical results for ImageNet subsets rather than fitted parameters renamed as predictions or self-citation chains. The method is self-contained, with no load-bearing self-citations or ansatzes smuggled in; external benchmarks and reported accuracy gains provide independent support.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

Assessment based solely on abstract; no explicit free parameters, invented entities, or detailed axioms are stated. The approach implicitly relies on standard assumptions about diffusion models and optimal transport.

axioms (2)

domain assumption Conditional likelihood optimization can achieve semantic matching without auxiliary classifiers
Central to the semantic matching component described in the abstract
domain assumption Optimal transport effectively aligns synthetic and target data distributions for distillation
Used as the basis for the distribution matching approach

pith-pipeline@v0.9.0 · 5533 in / 1231 out tokens · 59981 ms · 2026-05-07T17:35:18.144645+00:00 · methodology

discussion (0)

Forward citations

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Reference graph

Works this paper leans on

87 extracted references · 20 canonical work pages · cited by 1 Pith paper · 5 internal anchors

[1]

GPT-4 Technical Report

Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ah- mad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. Gpt-4 technical report.arXiv preprint arXiv:2303.08774,

work page internal anchor Pith review arXiv
[2]

Geometric dataset distances via optimal transport.Advances in Neural Infor- mation Processing Systems, 33:21428–21439, 2020

David Alvarez-Melis and Nicolo Fusi. Geometric dataset distances via optimal transport.Advances in Neural Infor- mation Processing Systems, 33:21428–21439, 2020. 8, 9

2020
[3]

Wasserstein generative adversarial networks

Martin Arjovsky et al. Wasserstein generative adversarial networks. InInternational conference on machine learning, pages 214–223. PMLR, 2017. 2, 4

2017
[4]

Learning probability measures with respect to optimal transport metrics.Advances in neural in- formation processing systems, 25, 2012

Guillermo Canas et al. Learning probability measures with respect to optimal transport metrics.Advances in neural in- formation processing systems, 25, 2012. 1, 5, 6

2012
[5]

Dimension-reduction attack! video generative models are experts on controllable image synthesis.arXiv preprint arXiv:2505.23325, 2025a

Hengyuan Cao, Yutong Feng, Biao Gong, Yijing Tian, Yun- hong Lu, Chuang Liu, and Bin Wang. Dimension-reduction attack! video generative models are experts on controllable image synthesis.arXiv preprint arXiv:2505.23325, 2025. 1

work page arXiv 2025
[6]

Dataset distillation by matching training trajectories

George Cazenavette, Tongzhou Wang, Antonio Torralba, Alexei A Efros, and Jun-Yan Zhu. Dataset distillation by matching training trajectories. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4750–4759, 2022. 1, 2

2022
[7]

Generalizing dataset distillation via deep generative prior

George Cazenavette, Tongzhou Wang, Antonio Torralba, Alexei A Efros, and Jun-Yan Zhu. Generalizing dataset distillation via deep generative prior. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3739–3748, 2023. 2, 5, 6, 7, 11

2023
[8]

Mgd 3: Mode- guided dataset distillation using diffusion models.arXiv preprint arXiv:2505.18963, 2025

Jeffrey A Chan-Santiago, Praveen Tirupattur, Gaurav Kumar Nayak, Gaowen Liu, and Mubarak Shah. Mgd 3: Mode- guided dataset distillation using diffusion models.arXiv preprint arXiv:2505.18963, 2025. 1, 2, 6, 7, 8, 4, 9, 10, 11

work page arXiv 2025
[9]

Rapverse: Coherent vocals and whole- body motion generation from text

Jiaben Chen, Xin Yan, Yihang Chen, Siyuan Cen, Zixin Wang, Qinwei Ma, Haoyu Zhen, Kaizhi Qian, Lie Lu, and Chuang Gan. Rapverse: Coherent vocals and whole- body motion generation from text. InProceedings of the IEEE/CVF International Conference on Computer Vision, pages 10097–10107, 2025. 14

2025
[10]

Influence-guided diffusion for dataset distillation

Mingyang Chen, Jiawei Du, Bo Huang, Yi Wang, Xiaobo Zhang, and Wei Wang. Influence-guided diffusion for dataset distillation. InThe Thirteenth International Conference on Learning Representations, 2025. 2, 4

2025
[11]

Optimal transport for domain adaptation.IEEE transactions on pattern analysis and machine intelligence, 39(9):1853–1865, 2016

Nicolas Courty, R ´emi Flamary, Devis Tuia, and Alain Rako- tomamonjy. Optimal transport for domain adaptation.IEEE transactions on pattern analysis and machine intelligence, 39(9):1853–1865, 2016. 3

2016
[12]

Diffusion models in vision: A survey

Florinel-Alin Croitoru, Vlad Hondru, Radu Tudor Ionescu, and Mubarak Shah. Diffusion models in vision: A survey. IEEE transactions on pattern analysis and machine intelli- gence, 45(9):10850–10869, 2023. 2

2023
[13]

Scaling up dataset distillation to imagenet-1k with constant memory

Justin Cui, Ruochen Wang, Si Si, and Cho-Jui Hsieh. Scaling up dataset distillation to imagenet-1k with constant memory. InInternational Conference on Machine Learning, pages 6565–6590. PMLR, 2023. 2

2023
[14]

Optical: Leveraging optimal transport for con- tribution allocation in dataset distillation

Xiao Cui, Yulei Qin, Wengang Zhou, Hongsheng Li, and Houqiang Li. Optical: Leveraging optimal transport for con- tribution allocation in dataset distillation. InProceedings of the Computer Vision and Pattern Recognition Conference, pages 15245–15254, 2025. 2

2025
[15]

Optimizing distributional geometry align- ment with optimal transport for generative dataset distilla- tion.arXiv preprint arXiv:2512.00308, 2025

Xiao Cui, Yulei Qin, Wengang Zhou, Hongsheng Li, and Houqiang Li. Optimizing distributional geometry align- ment with optimal transport for generative dataset distilla- tion.arXiv preprint arXiv:2512.00308, 2025. 2

work page arXiv 2025
[16]

Sinkhorn distances: Lightspeed computation of optimal transport.Advances in neural information pro- cessing systems, 26, 2013

Marco Cuturi. Sinkhorn distances: Lightspeed computation of optimal transport.Advances in neural information pro- cessing systems, 26, 2013. 3, 5

2013
[17]

Imagenet: A large-scale hierarchical image database

Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In2009 IEEE conference on computer vision and pattern recognition, pages 248–255. Ieee, 2009. 6

2009
[18]

Sequential subset matching for dataset distillation.Advances in Neural Infor- mation Processing Systems, 36:67487–67504, 2023

Jiawei Du, Qin Shi, and Joey Tianyi Zhou. Sequential subset matching for dataset distillation.Advances in Neural Infor- mation Processing Systems, 36:67487–67504, 2023. 2

2023
[19]

Inter- polating between optimal transport and mmd using sinkhorn divergences

Jean Feydy, Thibault S ´ejourn´e, Franc ¸ois-Xavier Vialard, Shun-ichi Amari, Alain Trouv ´e, and Gabriel Peyr ´e. Inter- polating between optimal transport and mmd using sinkhorn divergences. InThe 22nd international conference on arti- ficial intelligence and statistics, pages 2681–2690. PMLR,
[20]

Generative adversarial nets.Advances in neural information processing systems, 27, 2014

Ian J Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets.Advances in neural information processing systems, 27, 2014. 2

2014
[21]

Optimum quantization and its applications

Peter M Gruber. Optimum quantization and its applications. Advances in Mathematics, 186(2):456–497, 2004. 5

2004
[22]

Efficient dataset distillation via minimax diffusion

Jianyang Gu, Saeed Vahidian, Vyacheslav Kungurtsev, Hao- nan Wang, Wei Jiang, Yang You, and Yiran Chen. Efficient dataset distillation via minimax diffusion. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15793–15803, 2024. 1, 2, 6, 7, 8, 4, 9, 10, 11

2024
[23]

arXiv preprint arXiv:2310.05773 , year=

Ziyao Guo, Kai Wang, George Cazenavette, Hui Li, Kaipeng Zhang, and Yang You. Towards lossless dataset distillation via difficulty-aligned trajectory matching.arXiv preprint arXiv:2310.05773, 2023. 2

work page arXiv 2023
[24]

Deep residual learning for image recognition

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. InProceed- ings of the IEEE conference on computer vision and pattern recognition, pages 770–778, 2016. 9

2016
[25]

Classifier-Free Diffusion Guidance

Jonathan Ho and Tim Salimans. Classifier-free diffusion guidance.arXiv preprint arXiv:2207.12598, 2022. 2, 4

work page internal anchor Pith review arXiv 2022
[26]

Denoising diffusion probabilistic mod- els.Advances in neural information processing systems, 33: 6840–6851, 2020

Jonathan Ho et al. Denoising diffusion probabilistic mod- els.Advances in neural information processing systems, 33: 6840–6851, 2020. 2, 3, 1

2020
[27]

Imagenette: A smaller subset of 10 easily classified classes from imagenet, 2019

Jeremy Howard. Imagenette: A smaller subset of 10 easily classified classes from imagenet, 2019. 9

2019
[28]

Dataset condensation via efficient synthetic- data parameterization

Jang-Hyun Kim, Jinuk Kim, Seong Joon Oh, Sangdoo Yun, Hwanjun Song, Joonhyun Jeong, Jung-Woo Ha, and Hyun Oh Song. Dataset condensation via efficient synthetic- data parameterization. InInternational Conference on Ma- chine Learning, pages 11102–11118. PMLR, 2022. 2, 9, 10, 11

2022
[29]

Auto-Encoding Variational Bayes

Diederik P Kingma and Max Welling. Auto-encoding varia- tional bayes.arXiv preprint arXiv:1312.6114, 2013. 3, 1

work page internal anchor Pith review arXiv 2013
[30]

Domain adaptation and entanglement: an opti- mal transport perspective.arXiv preprint arXiv:2503.08155,

Okan Koc ¸, Alexander Soen, Chao-Kai Chiang, and Masashi Sugiyama. Domain adaptation and entanglement: an opti- mal transport perspective.arXiv preprint arXiv:2503.08155,

work page arXiv
[31]

Dataset distillation from first principles: Integrating core in- formation extraction and purposeful learning, 2024

Vyacheslav Kungurtsev, Yuanfang Peng, Jianyang Gu, Saeed Vahidian, Anthony Quinn, Fadwa Idlahcen, and Yiran Chen. Dataset distillation from first principles: Integrating core in- formation extraction and purposeful learning, 2024. 1

2024
[32]

A comprehensive survey of dataset distillation.IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(1):17–32, 2023

Shiye Lei and Dacheng Tao. A comprehensive survey of dataset distillation.IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(1):17–32, 2023. 1

2023
[33]

Towards un- derstanding camera motions in any video.arXiv preprint arXiv:2504.15376,

Zhiqiu Lin, Siyuan Cen, Daniel Jiang, Jay Karhade, Hewei Wang, Chancharik Mitra, Tiffany Ling, Yuhan Huang, Sifan Liu, Mingyu Chen, et al. Towards understanding camera mo- tions in any video.arXiv preprint arXiv:2504.15376, 2025. 1

work page arXiv 2025
[34]

Dataset distillation by automatic training trajecto- ries

Dai Liu, Jindong Gu, Hu Cao, Carsten Trinitis, and Martin Schulz. Dataset distillation by automatic training trajecto- ries. InEuropean Conference on Computer Vision, pages 334–351. Springer, 2024. 2

2024
[35]

Dataset distillation via the wasserstein metric.arXiv preprint arXiv:2311.18531,

Haoyang Liu, Yijiang Li, Tiancheng Xing, Vibhu Dalal, Luwei Li, Jingrui He, and Haohan Wang. Dataset distillation via the wasserstein metric.arXiv preprint arXiv:2311.18531,

work page arXiv
[36]

Dataset distillation via factorization.Ad- vances in neural information processing systems, 35:1100– 1113, 2022

Songhua Liu, Kai Wang, Xingyi Yang, Jingwen Ye, and Xinchao Wang. Dataset distillation via factorization.Ad- vances in neural information processing systems, 35:1100– 1113, 2022. 2

2022
[37]

Dream: Efficient dataset distillation by rep- resentative matching

Yanqing Liu, Jianyang Gu, Kai Wang, Zheng Zhu, Wei Jiang, and Yang You. Dream: Efficient dataset distillation by rep- resentative matching. InProceedings of the IEEE/CVF In- ternational Conference on Computer Vision, pages 17314– 17324, 2023. 6, 2

2023
[38]

Discovery of the reward function for embod- ied reinforcement learning agents.Nature Communications, 16(1):11064, 2025

Renzhi Lu, Zonghe Shao, Yuemin Ding, Ruijuan Chen, Don- grui Wu, Housheng Su, Tao Yang, Fumin Zhang, Jun Wang, Yang Shi, et al. Discovery of the reward function for embod- ied reinforcement learning agents.Nature Communications, 16(1):11064, 2025. 14

2025
[39]

Inpo: Inversion preference optimization with reparametrized ddim for efficient diffusion model alignment

Yunhong Lu, Qichao Wang, Hengyuan Cao, Xierui Wang, Xiaoyin Xu, and Min Zhang. Inpo: Inversion preference optimization with reparametrized ddim for efficient diffusion model alignment. InProceedings of the Computer Vision and Pattern Recognition Conference, pages 28629–28639, 2025. 1

2025
[40]

Discovery of the reward function for embodied reinforcement learning agents.Nature Communications, 16(1):11064, 2025a

Yunhong Lu, Qichao Wang, Hengyuan Cao, Xiaoyin Xu, and Min Zhang. Smoothed preference optimization via renoise inversion for aligning diffusion models with varied human preferences.arXiv preprint arXiv:2506.02698, 2025. 1

work page arXiv 2025
[41]

Reward forcing: Efficient streaming video generation with rewarded distribution matching distillation.arXiv preprint arXiv:2512.04678,

Yunhong Lu, Yanhong Zeng, Haobo Li, Hao Ouyang, Qi- uyu Wang, Ka Leong Cheng, Jiapeng Zhu, Hengyuan Cao, Zhipeng Zhang, Xing Zhu, et al. Reward forcing: Ef- ficient streaming video generation with rewarded distribu- tion matching distillation.arXiv preprint arXiv:2512.04678,

work page arXiv
[42]

Visualizing data using t-sne.Journal of machine learning research, 9 (Nov):2579–2605, 2008

Laurens van der Maaten and Geoffrey Hinton. Visualizing data using t-sne.Journal of machine learning research, 9 (Nov):2579–2605, 2008. 9

2008
[43]

Recent advances in optimal transport for machine learning.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024

Eduardo Fernandes Montesuma et al. Recent advances in optimal transport for machine learning.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024. 3

2024
[44]

Unlocking dataset distillation with diffusion models.arXiv preprint arXiv:2403.03881,

Brian B Moser, Federico Raue, Sebastian Palacio, Stanislav Frolov, and Andreas Dengel. Unlocking dataset distillation with diffusion models.arXiv preprint arXiv:2403.03881,

work page arXiv
[45]

Reliable fidelity and diversity metrics for generative models

Muhammad Ferjad Naeem, Seong Joon Oh, Youngjung Uh, Yunjey Choi, and Jaejun Yoo. Reliable fidelity and diversity metrics for generative models. InInternational conference on machine learning, pages 7176–7185. PMLR, 2020. 8, 9

2020
[46]

fast-pytorch-kmeans, 2020

Sehban Omer. fast-pytorch-kmeans, 2020. 8

2020
[47]

On aliased resizing and surprising subtleties in gan evaluation

Gaurav Parmar, Richard Zhang, and Jun-Yan Zhu. On aliased resizing and surprising subtleties in gan evaluation. InProceedings of the IEEE/CVF conference on computer vi- sion and pattern recognition, pages 11410–11420, 2022. 9

2022
[48]

Scalable diffusion models with trans- formers

William Peebles et al. Scalable diffusion models with trans- formers. InProceedings of the IEEE/CVF international con- ference on computer vision, pages 4195–4205, 2023. 6, 7, 8, 9, 11

2023
[49]

High-resolution image synthesis with latent diffusion models

Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bj ¨orn Ommer. High-resolution image synthesis with latent diffusion models. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684–10695, 2022. 3, 1

2022
[50]

Cads: Unleashing the diversity of diffusion models through condition-annealed sampling.arXiv preprint arXiv:2310.17347, 2023

Seyedmorteza Sadat, Jakob Buhmann, Derek Bradley, Otmar Hilliges, and Romann M Weber. Cads: Unleashing the di- versity of diffusion models through condition-annealed sam- pling.arXiv preprint arXiv:2310.17347, 2023. 4, 5

work page arXiv 2023
[51]

Laion-5b: An open large-scale dataset for training next generation image-text models.Advances in neural in- formation processing systems, 35:25278–25294, 2022

Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Worts- man, et al. Laion-5b: An open large-scale dataset for training next generation image-text models.Advances in neural in- formation processing systems, 35:25278–25294, 2022. 1

2022
[52]

Generalized large-scale data condensa- tion via various backbone and statistical matching

Shitong Shao, Zeyuan Yin, Muxin Zhou, Xindong Zhang, and Zhiqiang Shen. Generalized large-scale data condensa- tion via various backbone and statistical matching. InPro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16709–16718, 2024. 6, 8, 2, 4

2024
[53]

A novel data-driven lstm-saf model for power systems transient stability assessment.IEEE Transac- tions on Industrial Informatics, 20(7):9083–9097, 2024

Zonghe Shao, Qichao Wang, Yuzhe Cao, Defu Cai, Yang You, and Renzhi Lu. A novel data-driven lstm-saf model for power systems transient stability assessment.IEEE Transac- tions on Industrial Informatics, 20(7):9083–9097, 2024. 14

2024
[54]

Delt: A simple diversity-driven earlylate training for dataset distillation

Zhiqiang Shen, Ammar Sherif, Zeyuan Yin, and Shitong Shao. Delt: A simple diversity-driven earlylate training for dataset distillation. InProceedings of the Computer Vi- sion and Pattern Recognition Conference, pages 4797–4806,
[55]

Ot-clip: Understanding and general- izing clip via optimal transport

Liangliang Shi et al. Ot-clip: Understanding and general- izing clip via optimal transport. InForty-first International Conference on Machine Learning, 2024. 6

2024
[56]

Distilling dataset into neural field.arXiv preprint arXiv:2503.04835, 2025

Donghyeok Shin, HeeSun Bae, Gyuwon Sim, Wanmo Kang, and Il-Chul Moon. Distilling dataset into neural field.arXiv preprint arXiv:2503.04835, 2025. 2

work page arXiv 2025
[57]

Loss-curvature matching for dataset selection and condensation

Seungjae Shin, Heesun Bae, Donghyeok Shin, Weonyoung Joo, and Il-Chul Moon. Loss-curvature matching for dataset selection and condensation. InInternational Conference on Artificial Intelligence and Statistics, pages 8606–8628. PMLR, 2023. 2

2023
[58]

Very Deep Convolutional Networks for Large-Scale Image Recognition

Karen Simonyan and Andrew Zisserman. Very deep convo- lutional networks for large-scale image recognition.arXiv preprint arXiv:1409.1556, 2014. 9

work page internal anchor Pith review arXiv 2014
[59]

Denoising Diffusion Implicit Models

Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models.arXiv preprint arXiv:2010.02502, 2020. 1, 2

work page internal anchor Pith review arXiv 2010
[60]

Dˆ4: Dataset distillation via disentangled diffusion model

Duo Su, Junjie Hou, Weizhi Gao, Yingjie Tian, and Bowen Tang. Dˆ4: Dataset distillation via disentangled diffusion model. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 5809–5818, 2024. 2, 6, 8, 4, 9

2024
[61]

On the diversity and realism of distilled dataset: An efficient dataset distilla- tion paradigm

Peng Sun, Bei Shi, Daiwei Yu, and Tao Lin. On the diversity and realism of distilled dataset: An efficient dataset distilla- tion paradigm. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9390– 9399, 2024. 6, 8, 2, 4, 9

2024
[62]

Discriminative adversarial domain adaptation

Hui Tang and Kui Jia. Discriminative adversarial domain adaptation. InProceedings of the AAAI conference on artifi- cial intelligence, pages 5940–5947, 2020. 3

2020
[63]

Minority-focused text-to- image generation via prompt optimization

Soobin Um and Jong Chul Ye. Minority-focused text-to- image generation via prompt optimization. InProceedings of the Computer Vision and Pattern Recognition Conference, pages 20926–20936, 2025. 2, 4

2025
[64]

Springer, 2008

C ´edric Villani et al.Optimal transport: old and new. Springer, 2008. 3

2008
[65]

Dim: Distilling dataset into genera- tive model, 2023

Kai Wang, Jianyang Gu, Daquan Zhou, Zheng Zhu, Wei Jiang, and Yang You. Dim: Distilling dataset into genera- tive model, 2023. 2

2023
[66]

Dataset dis- tillation with neural characteristic function: A minmax per- spective

Shaobo Wang, Yicun Yang, Zhiyuan Liu, Chenghao Sun, Xuming Hu, Conghui He, and Linfeng Zhang. Dataset dis- tillation with neural characteristic function: A minmax per- spective. InProceedings of the Computer Vision and Pattern Recognition Conference, pages 25570–25580, 2025. 2

2025
[67]

Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba, and Alexei A. Efros. Dataset distillation, 2020. 1

2020
[68]

Transferable attention for domain adapta- tion

Ximei Wang, Liang Li, Weirui Ye, Mingsheng Long, and Jianmin Wang. Transferable attention for domain adapta- tion. InProceedings of the AAAI conference on artificial intelligence, pages 5345–5352, 2019. 3

2019
[69]

Herding dynamical weights to learn

Max Welling. Herding dynamical weights to learn. InPro- ceedings of the 26th annual international conference on ma- chine learning, pages 1121–1128, 2009. 10

2009
[70]

Are large-scale soft labels nec- essary for large-scale dataset distillation?arXiv preprint arXiv:2410.15919, 2024

Lingao Xiao and Yang He. Are large-scale soft labels nec- essary for large-scale dataset distillation?arXiv preprint arXiv:2410.15919, 2024. 2, 4

work page arXiv 2024
[71]

Difffit: Un- locking transferability of large diffusion models via sim- ple parameter-efficient fine-tuning

Enze Xie, Lewei Yao, Han Shi, Zhili Liu, Daquan Zhou, Zhaoqiang Liu, Jiawei Li, and Zhenguo Li. Difffit: Un- locking transferability of large diffusion models via sim- ple parameter-efficient fine-tuning. InProceedings of the IEEE/CVF International Conference on Computer Vision, pages 4230–4239, 2023. 2

2023
[72]

Towards adversarially robust dataset distillation by curvature regularization

Eric Xue, Yijiang Li, Haoyang Liu, Peiran Wang, Yifan Shen, and Haohan Wang. Towards adversarially robust dataset distillation by curvature regularization. InProceed- ings of the AAAI Conference on Artificial Intelligence, pages 9041–9049, 2025. 2

2025
[73]

What is dataset distillation learning?, 2024

William Yang, Ye Zhu, Zhiwei Deng, and Olga Russakovsky. What is dataset distillation learning?, 2024. 1

2024
[74]

Tfg: Unified training-free guidance for diffusion mod- els.Advances in Neural Information Processing Systems, 37: 22370–22417, 2024

Haotian Ye, Haowei Lin, Jiaqi Han, Minkai Xu, Sheng Liu, Yitao Liang, Jianzhu Ma, James Y Zou, and Stefano Er- mon. Tfg: Unified training-free guidance for diffusion mod- els.Advances in Neural Information Processing Systems, 37: 22370–22417, 2024. 8

2024
[75]

Squeeze, recover and relabel: Dataset condensation at imagenet scale from a new perspective

Zeyuan Yin et al. Squeeze, recover and relabel: Dataset condensation at imagenet scale from a new perspective. Advances in Neural Information Processing Systems, 36: 73582–73603, 2023. 1, 4, 6, 8, 2

2023
[76]

Freedom: Training-free energy-guided condi- tional diffusion model

Jiwen Yu, Yinhuai Wang, Chen Zhao, Bernard Ghanem, and Jian Zhang. Freedom: Training-free energy-guided condi- tional diffusion model. InProceedings of the IEEE/CVF In- ternational Conference on Computer Vision, pages 23174– 23184, 2023. 3, 4, 5, 1, 6, 8, 12

2023
[77]

Dataset condensation via generative model, 2023

David Junhao Zhang, Heng Wang, Chuhui Xue, Rui Yan, Wenqing Zhang, Song Bai, and Mike Zheng Shou. Dataset condensation via generative model, 2023. 2

2023
[78]

Gsdd: generative space dataset distillation for image super-resolution

Haiyu Zhang, Shaolin Su, Yu Zhu, Jinqiu Sun, and Yanning Zhang. Gsdd: generative space dataset distillation for image super-resolution. InProceedings of the AAAI Conference on Artificial Intelligence, pages 7069–7077, 2024. 2

2024
[79]

Spherical geometry diffusion: Generating high-quality 3d face geometry via sphere-anchored representations.arXiv preprint arXiv:2601.13371, 2026

Junyi Zhang, Yiming Wang, Yunhong Lu, Qichao Wang, Wenzhe Qian, Xiaoyin Xu, David Gu, and Min Zhang. Spherical geometry diffusion: Generating high-quality 3d face geometry via sphere-anchored representations.arXiv preprint arXiv:2601.13371, 2026. 1

work page arXiv 2026
[80]

Dataset condensation with differ- entiable siamese augmentation

Bo Zhao and Hakan Bilen. Dataset condensation with differ- entiable siamese augmentation. InInternational Conference on Machine Learning, pages 12674–12685. PMLR, 2021. 2

2021

Showing first 80 references.