Paper Citation Record · LEDGER

Dataset Distillation Based on Saliency-Driven Prototype Alignment

As of 15 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 0 inbound Pith citation observations for arXiv:2607.25318.

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

pith.paper-citation-record.v1
2607.25318 v2

Coverage vector

measured 45 of 45 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T01:50:48.042221Z

measured 45 of 45 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

45 of 45 outbound references displayed

verified exact0
verified fuzzy0
unresolved45
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f8417f14-49c2-4758-9061-fe9d99114440 · outbound

This paper cites Coreset selection for object detection.

Dataset Distillation Based on Saliency-Driven Prototype Alignment Coreset selection for object detection

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-03T01:50:43.402933Z

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Observation a98e4ffd-d2d3-42ea-be35-9aef3dadd792 · outbound

This paper cites Herding dynamical weights to learn.

Dataset Distillation Based on Saliency-Driven Prototype Alignment Herding dynamical weights to learn

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-03T01:50:43.501735Z

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Observation 50c44b4d-0b29-4601-90f4-9735e3fdfec5 · outbound

This paper cites An Empirical Study of Example Forgetting during Deep Neural Network Learning.

Dataset Distillation Based on Saliency-Driven Prototype Alignment An Empirical Study of Example Forgetting during Deep Neural Network Learning

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-03T01:50:43.565169Z

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Observation 379cf9c8-7172-4196-8275-dc22ef990d72 · outbound

This paper cites Dataset Distillation.

Dataset Distillation Based on Saliency-Driven Prototype Alignment Dataset Distillation

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-03T01:50:43.747255Z

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Observation 8b7e7077-3dee-4183-b8d9-4d7c7531d1af · outbound

This paper cites Data distillation: A survey.

Dataset Distillation Based on Saliency-Driven Prototype Alignment Data distillation: A survey

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-03T01:50:43.901888Z

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Observation a2f48124-e40f-4f20-bf44-4300742a0617 · outbound

This paper cites Emphasizing discriminative features for dataset distillation in complex scenarios.

Dataset Distillation Based on Saliency-Driven Prototype Alignment Emphasizing discriminative features for dataset distillation in complex scenarios

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-03T01:50:44.033623Z

Source-reported events for the cited work

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Observation 5dadfb25-9158-417a-9a7e-38e81cd547cf · outbound

This paper cites The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions.

Dataset Distillation Based on Saliency-Driven Prototype Alignment The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-03T01:50:44.176823Z

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Observation 32ecdf29-7bd0-4347-a5c1-6c5eedb0edc3 · outbound

This paper cites Mind the boundary: Coreset selection via reconstructing the decision boundary.

Dataset Distillation Based on Saliency-Driven Prototype Alignment Mind the boundary: Coreset selection via reconstructing the decision boundary

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-03T01:50:44.331683Z

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Observation 687254c2-24ac-4e19-ac8b-845debb0bce3 · outbound

This paper cites A coreset selection of coreset selection literature: Introduction and recent advances.arXiv preprint arXiv:2505.17799, 2025.

Dataset Distillation Based on Saliency-Driven Prototype Alignment A coreset selection of coreset selection literature: Introduction and recent advances.arXiv preprint arXiv:2505.17799, 2025

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-03T01:50:44.468805Z

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Observation c2bb952b-9c74-4d41-9a20-e7ace3cd65b8 · outbound

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

Dataset Distillation Based on Saliency-Driven Prototype Alignment CAFE: Learning to condense dataset by aligning features

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-03T01:50:44.652533Z

Source-reported events for the cited work

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Observation 615de72a-960c-4ef8-9d40-bacc77eae597 · outbound

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

Dataset Distillation Based on Saliency-Driven Prototype Alignment A comprehensive survey to dataset distillation.IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(1):17–32, 2023

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-03T01:50:44.804380Z

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Observation 0cccd56f-b3be-4f7e-bcb7-ba03e89f5fbb · outbound

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

Dataset Distillation Based on Saliency-Driven Prototype Alignment Teddy: Efficient large-scale dataset distillation via taylor-approximated matching

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-03T01:50:44.985773Z

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Observation 5353422d-1f74-4049-8aa1-559d02574040 · outbound

This paper cites Squeeze, recover and relabel: Dataset condensation at imagenet scale from a new perspective.

Dataset Distillation Based on Saliency-Driven Prototype Alignment Squeeze, recover and relabel: Dataset condensation at imagenet scale from a new perspective

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-03T01:50:45.143275Z

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Observation e391d952-a791-43a9-89f6-f9dd0b2a9353 · outbound

This paper cites Dataset distillation via curriculum data synthesis in large data era.Transactions on Machine Learning Research, 2024.

Dataset Distillation Based on Saliency-Driven Prototype Alignment Dataset distillation via curriculum data synthesis in large data era.Transactions on Machine Learning Research, 2024

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-03T01:50:45.335548Z

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Observation e5417f71-6b93-48ad-8680-9c6d1f78d4c1 · outbound

This paper cites Curriculum dataset distillation.IEEE Transactions on Image Processing, pages 4176–4187, 2025.

Dataset Distillation Based on Saliency-Driven Prototype Alignment Curriculum dataset distillation.IEEE Transactions on Image Processing, pages 4176–4187, 2025

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-03T01:50:45.452061Z

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Observation dc176f30-8014-4d63-9c5b-3193bb293a5c · outbound

This paper cites Synthesizing informative training samples with gan.

Dataset Distillation Based on Saliency-Driven Prototype Alignment Synthesizing informative training samples with gan

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-03T01:50:45.609207Z

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Observation 41f8bd31-d0ae-4f68-8753-879efc605d74 · outbound

This paper cites DiM: Distilling Dataset into Generative Model.

Dataset Distillation Based on Saliency-Driven Prototype Alignment DiM: Distilling Dataset into Generative Model

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-03T01:50:45.707639Z

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Observation 7410e98b-a2e3-4242-afb0-bfc6ecba3162 · outbound

This paper cites Hierarchical Features Matter: A deep exploration of gan priors for improved dataset distillation.

Dataset Distillation Based on Saliency-Driven Prototype Alignment Hierarchical Features Matter: A deep exploration of gan priors for improved dataset distillation

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-03T01:50:45.842889Z

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Observation bf8b00c7-3bea-485a-92a4-2cfe458554f3 · outbound

This paper cites D4M: Dataset distillation via disentangled diffusion model.

Dataset Distillation Based on Saliency-Driven Prototype Alignment D4M: Dataset distillation via disentangled diffusion model

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-03T01:50:45.975300Z

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Observation 8574e8a7-b04e-45a2-bfca-1c60f5496b4f · outbound

This paper cites Efficient dataset distillation via minimax diffusion.

Dataset Distillation Based on Saliency-Driven Prototype Alignment Efficient dataset distillation via minimax diffusion

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-03T01:50:46.057775Z

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Observation 290f3e0c-05b9-488c-92c3-2e4c1d85bfe4 · outbound

This paper cites Mgd 3: Mode-guided dataset distillation using diffusion models.

Dataset Distillation Based on Saliency-Driven Prototype Alignment Mgd 3: Mode-guided dataset distillation using diffusion models

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-03T01:50:46.136025Z

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Observation e2825733-9b32-4d1a-9bb2-66d7b83d8de3 · outbound

This paper cites Dataset distillation via vision-language category prototype.

Dataset Distillation Based on Saliency-Driven Prototype Alignment Dataset distillation via vision-language category prototype

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-03T01:50:46.264631Z

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Observation a83e4af4-4246-4879-80d8-916e02f25ae7 · outbound

This paper cites High-resolution image synthesis with latent diffusion models.

Dataset Distillation Based on Saliency-Driven Prototype Alignment High-resolution image synthesis with latent diffusion models

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-03T01:50:46.354341Z

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Observation 461e0230-0fb0-4a17-9896-8b03294ef7d6 · outbound

This paper cites Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks.

Dataset Distillation Based on Saliency-Driven Prototype Alignment Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-03T01:50:46.461975Z

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Observation 8f7eee9e-8ba0-444e-9d95-9bbf0be741db · outbound

This paper cites Privacy for free: How does dataset condensation help privacy? InProceedings of the International Conference on Machine Learning (ICML), pages 5378–5396, 2022.

Dataset Distillation Based on Saliency-Driven Prototype Alignment Privacy for free: How does dataset condensation help privacy? InProceedings of the International Conference on Machine Learning (ICML), pages 5378–5396, 2022

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-03T01:50:46.558293Z

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Observation 642bd1a9-3214-4dee-b593-5323e159dedc · outbound

This paper cites Improving noise efficiency in privacy-preserving dataset distillation.

Dataset Distillation Based on Saliency-Driven Prototype Alignment Improving noise efficiency in privacy-preserving dataset distillation

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-03T01:50:46.627377Z

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Observation 063a0aec-9cfe-4d92-b2ac-def635ddda79 · outbound

This paper cites Rethinking backdoor attacks on dataset distillation: A kernel method perspective.

Dataset Distillation Based on Saliency-Driven Prototype Alignment Rethinking backdoor attacks on dataset distillation: A kernel method perspective

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-03T01:50:46.689696Z

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Observation ab483684-3824-4fec-bb4c-b2e09f6a9f62 · outbound

This paper cites Medsynth: Leveraging generative model for healthcare data sharing.

Dataset Distillation Based on Saliency-Driven Prototype Alignment Medsynth: Leveraging generative model for healthcare data sharing

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-03T01:50:46.760232Z

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Observation 57bc2af4-d9b8-4870-95d1-8334a36e9c91 · outbound

This paper cites Image distillation for safe data sharing in histopathology.

Dataset Distillation Based on Saliency-Driven Prototype Alignment Image distillation for safe data sharing in histopathology

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-03T01:50:46.855287Z

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Observation e197044d-868e-497c-9c52-be79ad3d4535 · outbound

This paper cites Federated Learning via Synthetic Data.

Dataset Distillation Based on Saliency-Driven Prototype Alignment Federated Learning via Synthetic Data

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-03T01:50:46.986353Z

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Observation e44406da-9142-4217-9f96-591e60568177 · outbound

This paper cites FedVCK: Non-iid robust and communication-efficient federated learning via valuable condensed knowledge for medical image analysis.

Dataset Distillation Based on Saliency-Driven Prototype Alignment FedVCK: Non-iid robust and communication-efficient federated learning via valuable condensed knowledge for medical image analysis

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-03T01:50:47.076297Z

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Observation 4a2e9b60-807c-4758-b2a6-1d92aa08a3a6 · outbound

This paper cites Dataset meta- learning from kernel ridge-regression.

Dataset Distillation Based on Saliency-Driven Prototype Alignment Dataset meta- learning from kernel ridge-regression

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-03T01:50:47.146982Z

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Observation d55fcf42-7a70-4dd3-9d01-3e38faccf62a · outbound

This paper cites Dataset condensation with gradient matching.

Dataset Distillation Based on Saliency-Driven Prototype Alignment Dataset condensation with gradient matching

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-03T01:50:47.213306Z

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Observation 7939fd70-38c0-4396-8650-b8d7ffdf8e6a · outbound

This paper cites Efros, and Jun-Yan Zhu.

Dataset Distillation Based on Saliency-Driven Prototype Alignment Efros, and Jun-Yan Zhu

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-03T01:50:47.317894Z

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Observation a5f0eb63-3c4b-4d70-a1d9-d48ba5174f4f · outbound

This paper cites Towards stable and storage-efficient dataset distillation: Matching convexified trajectory.

Dataset Distillation Based on Saliency-Driven Prototype Alignment Towards stable and storage-efficient dataset distillation: Matching convexified trajectory

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-03T01:50:47.406071Z

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Observation 1ee6ae62-4310-4c80-904e-79c1ad534965 · outbound

This paper cites Dataset condensation with distribution matching.

Dataset Distillation Based on Saliency-Driven Prototype Alignment Dataset condensation with distribution matching

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-03T01:50:47.457941Z

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Observation 85838437-9158-404e-b797-af18a8530407 · outbound

This paper cites M3D: Dataset condensation by minimizing maximum mean discrepancy.

Dataset Distillation Based on Saliency-Driven Prototype Alignment M3D: Dataset condensation by minimizing maximum mean discrepancy

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-03T01:50:47.522842Z

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Observation 6525e09b-1710-43f1-817e-f951547a3500 · outbound

This paper cites Scalable diffusion models with transformers.

Dataset Distillation Based on Saliency-Driven Prototype Alignment Scalable diffusion models with transformers

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-03T01:50:47.590000Z

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Observation c2e429c7-d7cf-4e11-8640-5019ab0ae413 · outbound

This paper cites Dmgd: Train-free dataset distillation with semantic-distribution matching in diffusion models.

Dataset Distillation Based on Saliency-Driven Prototype Alignment Dmgd: Train-free dataset distillation with semantic-distribution matching in diffusion models

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-03T01:50:47.655262Z

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Observation 5b856305-3b0c-4b73-9f02-c501a301017b · outbound

This paper cites Self-supervised dataset distillation for transfer learning.

Dataset Distillation Based on Saliency-Driven Prototype Alignment Self-supervised dataset distillation for transfer learning

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-03T01:50:47.714661Z

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Observation ae915de2-e061-427a-a614-1b6b3072f0bd · outbound

This paper cites Generalizing dataset distillation via deep generative prior.

Dataset Distillation Based on Saliency-Driven Prototype Alignment Generalizing dataset distillation via deep generative prior

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-03T01:50:47.782065Z

Source-reported events for the cited work

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Observation 6591df8d-5b24-48bc-9bbb-cd7e0b6f09ac · outbound

This paper cites Dataset condensation via efficient synthetic-data parameterization.

Dataset Distillation Based on Saliency-Driven Prototype Alignment Dataset condensation via efficient synthetic-data parameterization

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-03T01:50:47.842714Z

Source-reported events for the cited work

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Observation 5394f750-f964-4dae-abca-7f94680ae561 · outbound

This paper cites Dataset distillation using neural feature regression.

Dataset Distillation Based on Saliency-Driven Prototype Alignment Dataset distillation using neural feature regression

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-03T01:50:47.906391Z

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Unavailable: canonical work link unavailable.

Observation 5db3b43a-1429-4b2a-8150-b15f858a1561 · outbound

This paper cites Dataset Distillation Based on Saliency-Driven Prototype Alignment.

Dataset Distillation Based on Saliency-Driven Prototype Alignment Dataset Distillation Based on Saliency-Driven Prototype Alignment

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-03T01:50:47.988781Z

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Observation fba0dd6b-c2ec-41f3-aa93-12f0cf870075 · outbound

This paper cites However, the performance does not increase monotonically with M, and the optimal ensemble size varies across IPC settings and architectures.

Dataset Distillation Based on Saliency-Driven Prototype Alignment However, the performance does not increase monotonically with M, and the optimal ensemble size varies across IPC settings and architectures

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-03T01:50:48.042221Z

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Pith citing papers

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