Paper Citation Record · LEDGER

The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions

As of 9 August 2026, this Paper Citation Record lists 100 of 208 outbound references and 16 inbound Pith citation observations for arXiv:2502.05673.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2502.05673 v3

Coverage vector

measured 100 of 208 reference resolution

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measured 116 of 116 standing notices

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measured 16 of 16 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

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Reference resolution

100 of 208 outbound references displayed

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External citation measurements

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Outbound references

Observation 2d3dad9d-a2dc-4bf8-a9d5-8faecfe67e2d · outbound

This paper cites Large Language Models: A Survey.

The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Large Language Models: A Survey

Reference 1

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Observation 0cbc3a20-5cb7-4a14-836c-fcd708c40a56 · outbound

This paper cites Vision-language models for vision tasks: A survey,.

The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Vision-language models for vision tasks: A survey,

Reference 2

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Observation 3c9c2a31-325c-46f4-9231-ea153192154b · outbound

This paper cites Learning transferable visual models from natural language supervision,.

The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Learning transferable visual models from natural language supervision,

Reference 3

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Observation 5779cc47-0a07-48d3-8bac-e1c48019fbe0 · outbound

This paper cites Dataset Distillation.

The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Dataset Distillation

Reference 4

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Observation c65856ff-3fb9-401f-9ec2-ac64a20ee6a1 · outbound

This paper cites What is dataset distillation learning?.

The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions What is dataset distillation learning?

Reference 5

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Observation 1ff92f73-ebc8-4ef7-a394-6747f4224b1c · outbound

This paper cites Imagenet large scale visual recognition challenge,.

The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Imagenet large scale visual recognition challenge,

Reference 6

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Observation f3c33dde-a778-4d24-936e-41482211d70e · outbound

This paper cites Imagenet-21k pretraining for the masses,.

The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Imagenet-21k pretraining for the masses,

Reference 7

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Observation a601b278-b5cd-4a85-a97f-45f4ba6ffa87 · outbound

This paper cites A survey on dataset distillation: Approaches, applications and future directions,.

The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions A survey on dataset distillation: Approaches, applications and future directions,

Reference 8

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Observation eef68b46-047b-49c2-960a-f235fa9344e5 · outbound

This paper cites A comprehensive survey of dataset distil- lation,.

The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions A comprehensive survey of dataset distil- lation,

Reference 9

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Observation 04c532d2-b012-4825-a0f7-2a2b3b9142a0 · outbound

This paper cites Dataset distillation: A comprehen- sive review,.

The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Dataset distillation: A comprehen- sive review,

Reference 10

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Observation 8c0f393c-9d16-45c6-8d80-77ec4f2b99e5 · outbound

This paper cites The mnist database of handwritten digit images for machine learning research [best of the web],.

The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions The mnist database of handwritten digit images for machine learning research [best of the web],

Reference 11

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Observation d2bf46d1-5fff-44c1-99a3-433c5450adb2 · outbound

This paper cites Backpropagation through time: what it does and how to do it,.

The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Backpropagation through time: what it does and how to do it,

Reference 12

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Observation 338cdf91-8ebf-4964-b527-4edb2e404c98 · outbound

This paper cites Embarrassingly simple dataset distillation,.

The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Embarrassingly simple dataset distillation,

Reference 13

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Observation 9a0daaf0-3c71-4655-9d8d-19714b240051 · outbound

This paper cites Teddy: Efficient large-scale dataset distillation via taylor-approximated matching,.

The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Teddy: Efficient large-scale dataset distillation via taylor-approximated matching,

Reference 14

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Observation baef0b03-dd78-4c83-b245-7768661dfc29 · outbound

This paper cites Dataset condensation with gradient matching,.

The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Dataset condensation with gradient matching,

Reference 15

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Observation 118e7d61-2171-4867-9a4e-1aaaac653c99 · outbound

This paper cites Dataset condensation with differentiable siamese augmentation,.

The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Dataset condensation with differentiable siamese augmentation,

Reference 16

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Observation 76c3abb9-2585-4c95-903a-3bdd6d082a1f · outbound

This paper cites Dataset distillation by matching training trajectories,.

The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Dataset distillation by matching training trajectories,

Reference 17

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Observation 7ff3feba-a716-44e6-8380-2b29de72fdf4 · outbound

This paper cites Minimizing the accumulated trajectory error to improve dataset distillation,.

The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Minimizing the accumulated trajectory error to improve dataset distillation,

Reference 18

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Observation 5f1d608f-0f78-4ffe-82ba-421d576ad4d0 · outbound

This paper cites Convolutional deep belief net- works on cifar-10,.

The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Convolutional deep belief net- works on cifar-10,

Reference 19

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Observation 05ffb01a-e011-48ba-888a-9b9974c98cea · outbound

This paper cites AST: Effective Dataset Distillation through Alignment with Smooth and High-Quality Expert Trajectories.

The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions AST: Effective Dataset Distillation through Alignment with Smooth and High-Quality Expert Trajectories

Reference 20

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Observation 102e3f5c-ca8c-4010-a0f3-7188bcaf11d8 · outbound

This paper cites To- wards stable and storage-efficient dataset distillation: Matching convexified trajectory,.

The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions To- wards stable and storage-efficient dataset distillation: Matching convexified trajectory,

Reference 21

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Observation 3f9732dd-2b5e-49b1-aa8a-efbcfba78021 · outbound

This paper cites Neural tangent kernel: Convergence and generalization in neural networks,.

The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Neural tangent kernel: Convergence and generalization in neural networks,

Reference 22

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This paper cites Dataset distillation using parameter pruning,.

The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Dataset distillation using parameter pruning,

Reference 23

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Observation f14bac48-1b10-4ea3-8a6d-4e318db26ab3 · outbound

This paper cites Scaling up dataset distillation to imagenet-1k with constant memory,.

The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Scaling up dataset distillation to imagenet-1k with constant memory,

Reference 24

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Observation 52a9a2dc-c3cb-43df-974e-ed98773e8126 · outbound

This paper cites Dataset distillation by automatic training trajectories,.

The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Dataset distillation by automatic training trajectories,

Reference 25

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This paper cites Efficient dataset distillation through low-rank space sampling,.

The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Efficient dataset distillation through low-rank space sampling,

Reference 26

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Observation 5ee71ac1-c90d-47f0-bc89-5fa9e0c565f5 · outbound

This paper cites Dataset condensation with distribution matching,.

The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Dataset condensation with distribution matching,

Reference 27

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This paper cites Cafe: Learning to condense dataset by aligning features,.

The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Cafe: Learning to condense dataset by aligning features,

Reference 28

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Observation 1f75e740-a1ec-4b55-83cc-c646cdc5dc98 · outbound

This paper cites Improved distribution match- ing for dataset condensation,.

The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Improved distribution match- ing for dataset condensation,

Reference 29

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This paper cites Dance: Dual-view distribution alignment for dataset condensation,.

The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Dance: Dual-view distribution alignment for dataset condensation,

Reference 30

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The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Decomposed distribution matching in dataset con- densation,

Reference 31

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The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Datadam: Efficient dataset distillation with attention matching,

Reference 32

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This paper cites M3d: Dataset condensation by minimizing maximum mean discrepancy,.

The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions M3d: Dataset condensation by minimizing maximum mean discrepancy,

Reference 33

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The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Berlinet and C

Reference 34

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This paper cites Dataset condensation with latent quantile matching,.

The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Dataset condensation with latent quantile matching,

Reference 35

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This paper cites Dataset Distillation via the Wasserstein Metric.

The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Dataset Distillation via the Wasserstein Metric

Reference 36

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The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Statistical aspects of wasserstein distances,

Reference 37

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This paper cites Exploiting inter-sample and inter-feature relations in dataset distillation,.

The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Exploiting inter-sample and inter-feature relations in dataset distillation,

Reference 38

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This paper cites Dataset distillation with neural characteristic function: A min- max perspective,.

The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Dataset distillation with neural characteristic function: A min- max perspective,

Reference 39

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The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Hyperbolic dataset distillation,

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The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Dataset Distillation in Latent Space

Reference 41

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This paper cites Generalizing dataset distillation via deep generative prior,.

The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Generalizing dataset distillation via deep generative prior,

Reference 42

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The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Stylegan-xl: Scaling style- gan to large diverse datasets,

Reference 43

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The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Dataset Distillation with Probabilistic Latent Features

Reference 44

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The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Frequency domain-based dataset distillation,

Reference 45

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The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Neural spectral decomposition for dataset distillation,

Reference 46

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This paper cites Distilling dataset into neural field,.

The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Distilling dataset into neural field,

Reference 47

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The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Understanding dataset distillation via spectral filtering,

Reference 48

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The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Sequential subset matching for dataset distillation,

Reference 49

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This paper cites Optical: Leveraging optimal transport for contribution allocation in dataset distilla- tion,.

The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Optical: Leveraging optimal transport for contribution allocation in dataset distilla- tion,

Reference 50

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The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Mutual information neural estima- tion,

Reference 51

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The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Mim4dd: Mutual information maximization for dataset distillation,

Reference 52

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This paper cites Going beyond feature similarity: Effective dataset distillation based on class-aware conditional mutual information,.

The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Going beyond feature similarity: Effective dataset distillation based on class-aware conditional mutual information,

Reference 53

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This paper cites Fyi: Flip your images for dataset distillation,.

The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Fyi: Flip your images for dataset distillation,

Reference 54

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This paper cites Can pre-trained models assist in dataset distillation?.

The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Can pre-trained models assist in dataset distillation?

Reference 55

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This paper cites Hierarchical features matter: A deep exploration of gan priors for improved dataset distillation,.

The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Hierarchical features matter: A deep exploration of gan priors for improved dataset distillation,

Reference 56

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The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Supervised contrastive learning,

Reference 57

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This paper cites Feature extraction by non-parametric mutual infor- mation maximization,.

The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Feature extraction by non-parametric mutual infor- mation maximization,

Reference 58

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This paper cites Generative adver- sarial nets,.

The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Generative adver- sarial nets,

Reference 59

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The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Diffusion models in vision: A survey,

Reference 60

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The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Squeeze, recover and relabel: Dataset condensation at imagenet scale from a new perspective,

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The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions A label is worth a thousand images in dataset distillation,

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Reference 63

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The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Generative dataset distillation: Balancing global structure and local details,

Reference 64

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Reference 65

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Reference 66

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The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Dˆ 4: Dataset distillation via disentangled diffusion model,

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The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Dataset distillation via vision-language category prototype,

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The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions One Category One Prompt: Dataset Distillation using Diffusion Models

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The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Diffusion-Augmented Coreset Expansion for Scalable Dataset Distillation

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The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions On the diversity and realism of distilled dataset: An efficient dataset distillation paradigm,

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Reference 74

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The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Taming diffusion for dataset distillation with high representativeness,

Reference 75

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Reference 76

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The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Influence-guided diffusion for dataset distillation,

Reference 78

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The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Scalable diffusion models with transform- ers,

Reference 79

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The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Mgd3: Mode-guided dataset distillation using diffu- sion models,

Reference 80

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The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Flow matching for generative modeling,

Reference 81

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Reference 82

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The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Batch normaliza- tion alleviates the spectral bias in coordinate networks,

Reference 83

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The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Dataset distillation in large data era,

Reference 84

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The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Self- paced curriculum learning,

Reference 85

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The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Self-supervised Dataset Distillation: A Good Compression Is All You Need

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The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Self-supervised multimodal learning: A survey,

Reference 87

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The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Generalized large-scale data condensation via various backbone and statistical matching,

Reference 88

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The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Elucidating the design space of dataset condensation,

Reference 89

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The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Dataset distillation via committee voting,

Reference 90

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The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions A fast knowledge distillation framework for visual recognition,

Reference 91

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The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions FocusDD: Real-World Scene Infusion for Robust Dataset Distillation

Reference 92

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The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions An image is worth 16x16 words: Transformers for image recognition at scale,

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The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Towards Consistent and Efficient Dataset Distillation via Diffusion-Driven Selection

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The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Gift: Unlocking full potential of labels in distilled dataset at near-zero cost,

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This paper cites Are large-scale soft labels necessary for large- scale dataset distillation?.

The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Are large-scale soft labels necessary for large- scale dataset distillation?

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The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Emphasizing dis- criminative features for dataset distillation in complex scenarios,

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The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Grad-cam: Visual explanations from deep networks via gradient-based localization,

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The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Enhancing dataset distillation via non-critical region refinement,

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The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Color-oriented redundancy reduction in dataset distillation,

Reference 100

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The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Importance-aware adaptive dataset distillation,

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Reference 40

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