Paper Citation Record · LEDGER

Taming Diffusion for Dataset Distillation with High Representativeness

As of 10 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 0 inbound Pith citation observations for arXiv:2505.18399.

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

pith.paper-citation-record.v1
2505.18399 v1

Coverage vector

measured 48 of 48 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:35:46.957390Z

measured 48 of 48 standing notices

One-hop event checks from named stored sources.

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Pith citing papers itemized under the disclosed page cap.

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

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Reference resolution

48 of 48 outbound references displayed

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External citation measurements

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Outbound references

Observation 929069f3-ea83-4228-bf3e-46360426bae6 · outbound

This paper cites write newline.

Taming Diffusion for Dataset Distillation with High Representativeness write newline

Reference 1

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Observation b87769d9-70cf-4b04-ba47-ec2b63c645ce · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Taming Diffusion for Dataset Distillation with High Representativeness An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 2

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Observation b91cfa69-9d9f-4f44-8f53-f2a8326dba4e · outbound

This paper cites Synthetic Data from Diffusion Models Improves ImageNet Classification.

Taming Diffusion for Dataset Distillation with High Representativeness Synthetic Data from Diffusion Models Improves ImageNet Classification

Reference 3

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This paper cites A., and Zhu, J.-Y.

Taming Diffusion for Dataset Distillation with High Representativeness A., and Zhu, J.-Y

Reference 4

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

Taming Diffusion for Dataset Distillation with High Representativeness Scaling up dataset distillation to imagenet-1k with constant memory

Reference 5

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This paper cites Imagenet: A large-scale hierarchical image database.

Taming Diffusion for Dataset Distillation with High Representativeness Imagenet: A large-scale hierarchical image database

Reference 6

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

Taming Diffusion for Dataset Distillation with High Representativeness Exploiting inter-sample and inter-feature relations in dataset distillation

Reference 7

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This paper cites and Nichol, A.

Taming Diffusion for Dataset Distillation with High Representativeness and Nichol, A

Reference 8

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Taming Diffusion for Dataset Distillation with High Representativeness Y., Zhou, J

Reference 9

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Taming Diffusion for Dataset Distillation with High Representativeness Unresolved cited work

Reference 10

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Observation 20517b34-ef74-4cc5-b0ed-52c70c52a5e1 · outbound

This paper cites Diversity-Driven Synthesis: Enhancing Dataset Distillation through Directed Weight Adjustment.

Taming Diffusion for Dataset Distillation with High Representativeness Diversity-Driven Synthesis: Enhancing Dataset Distillation through Directed Weight Adjustment

Reference 11

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This paper cites Scaling rectified flow transformers for high-resolution image synthesis.

Taming Diffusion for Dataset Distillation with High Representativeness Scaling rectified flow transformers for high-resolution image synthesis

Reference 12

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Observation b6caa33f-2f69-4eed-8897-49cfc8aaf242 · outbound

This paper cites Efficient Dataset Distillation via Minimax Diffusion.

Taming Diffusion for Dataset Distillation with High Representativeness Efficient Dataset Distillation via Minimax Diffusion

Reference 13

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Observation f6b1342a-d63c-42c5-81b0-e59511ebff6c · outbound

This paper cites Deep residual learning for image recognition.

Taming Diffusion for Dataset Distillation with High Representativeness Deep residual learning for image recognition

Reference 14

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This paper cites Denoising diffusion probabilistic models.

Taming Diffusion for Dataset Distillation with High Representativeness Denoising diffusion probabilistic models

Reference 15

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This paper cites A smaller subset of 10 easily classified classes from imagenet, and a little more french.

Taming Diffusion for Dataset Distillation with High Representativeness A smaller subset of 10 easily classified classes from imagenet, and a little more french

Reference 16

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This paper cites and Robbins, H.

Taming Diffusion for Dataset Distillation with High Representativeness and Robbins, H

Reference 17

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This paper cites P., Welling, M., et al.

Taming Diffusion for Dataset Distillation with High Representativeness P., Welling, M., et al

Reference 18

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This paper cites Learning multiple layers of features from tiny images.

Taming Diffusion for Dataset Distillation with High Representativeness Learning multiple layers of features from tiny images

Reference 19

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This paper cites and Yang, X.

Taming Diffusion for Dataset Distillation with High Representativeness and Yang, X

Reference 20

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Taming Diffusion for Dataset Distillation with High Representativeness Dataset condensation with contrastive signals

Reference 21

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Taming Diffusion for Dataset Distillation with High Representativeness and Chung, H

Reference 22

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This paper cites Large scale dataset distillation with domain shift.

Taming Diffusion for Dataset Distillation with High Representativeness Large scale dataset distillation with domain shift

Reference 23

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This paper cites Shufflenet v2: Practical guidelines for efficient cnn architecture design.

Taming Diffusion for Dataset Distillation with High Representativeness Shufflenet v2: Practical guidelines for efficient cnn architecture design

Reference 24

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Taming Diffusion for Dataset Distillation with High Representativeness and Tsang, W

Reference 25

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Taming Diffusion for Dataset Distillation with High Representativeness and Xie, S

Reference 26

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Taming Diffusion for Dataset Distillation with High Representativeness High-resolution image synthesis with latent diffusion models

Reference 27

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Taming Diffusion for Dataset Distillation with High Representativeness Mobilenetv2: Inverted residuals and linear bottlenecks

Reference 28

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This paper cites Generalized large-scale data condensation via various backbone and statistical matching.

Taming Diffusion for Dataset Distillation with High Representativeness Generalized large-scale data condensation via various backbone and statistical matching

Reference 29

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Taming Diffusion for Dataset Distillation with High Representativeness Lazydit: Lazy learning for the acceleration of diffusion transformers

Reference 30

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Taming Diffusion for Dataset Distillation with High Representativeness Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 31

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Taming Diffusion for Dataset Distillation with High Representativeness Denoising Diffusion Implicit Models

Reference 32

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This paper cites Beyond neural scaling laws: beating power law scaling via data pruning.

Taming Diffusion for Dataset Distillation with High Representativeness Beyond neural scaling laws: beating power law scaling via data pruning

Reference 33

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Taming Diffusion for Dataset Distillation with High Representativeness D 4M: Dataset Distillation via Disentangled Diffusion Model

Reference 34

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This paper cites On the diversity and realism of distilled dataset: An efficient dataset distillation paradigm.

Taming Diffusion for Dataset Distillation with High Representativeness On the diversity and realism of distilled dataset: An efficient dataset distillation paradigm

Reference 35

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Taming Diffusion for Dataset Distillation with High Representativeness Data pruning via moving-one-sample-out

Reference 36

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Taming Diffusion for Dataset Distillation with High Representativeness and Le, Q

Reference 37

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Taming Diffusion for Dataset Distillation with High Representativeness Training data-efficient image transformers & distillation through attention

Reference 38

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Taming Diffusion for Dataset Distillation with High Representativeness Cafe: Learning to condense dataset by aligning features

Reference 39

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Taming Diffusion for Dataset Distillation with High Representativeness Dataset Distillation

Reference 40

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Taming Diffusion for Dataset Distillation with High Representativeness Squeeze, Recover and Relabel: Dataset Condensation at ImageNet Scale From A New Perspective

Reference 41

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Observation 81f62682-e206-4c85-9606-39f146c7080c · outbound

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Taming Diffusion for Dataset Distillation with High Representativeness Teddy: Efficient large-scale dataset distillation via taylor-approximated matching

Reference 42

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Taming Diffusion for Dataset Distillation with High Representativeness M3D: Dataset Condensation by Minimizing Maximum Mean Discrepancy

Reference 43

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Taming Diffusion for Dataset Distillation with High Representativeness Unresolved cited work

Reference 44

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Taming Diffusion for Dataset Distillation with High Representativeness and Bilen, H

Reference 45

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Observation d39482ab-cde0-4cfc-bee7-90f0d8240661 · outbound

This paper cites and Bilen, H.

Taming Diffusion for Dataset Distillation with High Representativeness and Bilen, H

Reference 46

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Observation 3b8ffb0f-2a5f-4552-81b0-06fb2a516d46 · outbound

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Taming Diffusion for Dataset Distillation with High Representativeness Dataset Condensation with Gradient Matching

Reference 47

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This paper cites Improved distribution matching for dataset condensation.

Taming Diffusion for Dataset Distillation with High Representativeness Improved distribution matching for dataset condensation

Reference 48

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Pith citing papers

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