Paper Citation Record · LEDGER

Dataset Distillation via Vision-Language Category Prototype

As of 7 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 1 inbound Pith citation observation for arXiv:2506.23580.

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

pith.paper-citation-record.v1
2506.23580 v1

Coverage vector

measured 48 of 48 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:44:43.727987Z

measured 49 of 49 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T15:50:35.343444Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

48 of 48 outbound references displayed

verified exact1
verified fuzzy39
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4fb729e2-c903-45fe-93d5-9d9aa7fb31a9 · outbound

This paper cites A review of local outlier factor algorithms for out- lier detection in big data streams.

Dataset Distillation via Vision-Language Category Prototype A review of local outlier factor algorithms for out- lier detection in big data streams

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:44:50.910702Z

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Observation 3d3fb70b-8dc7-42ca-9dba-9e16432dc828 · outbound

This paper cites No Free Lunch in "Privacy for Free: How does Dataset Condensation Help Privacy".

Dataset Distillation via Vision-Language Category Prototype No Free Lunch in "Privacy for Free: How does Dataset Condensation Help Privacy"

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-08-06T21:44:44.064952Z

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Observation 8f51a83f-bdcc-473b-914e-d59c14dd5b7e · outbound

This paper cites Dataset distillation by matching training trajectories.

Dataset Distillation via Vision-Language Category Prototype Dataset distillation by matching training trajectories

Reference 3

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Observation 772be8a9-36b3-4c7b-a841-460a9409c152 · outbound

This paper cites Generalizing dataset distillation via deep generative prior.

Dataset Distillation via Vision-Language Category Prototype Generalizing dataset distillation via deep generative prior

Reference 4

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Observation ff06c555-bd62-45cf-a6d0-89ebe6fb496d · outbound

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

Dataset Distillation via Vision-Language Category Prototype Imagenet: A large-scale hierarchical im- age database

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:44:50.275500Z

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Observation 01a1013f-0bb0-4516-b4e4-77c71a025d00 · outbound

This paper cites Remember the past: Distilling datasets into addressable memories for neural net- works.

Dataset Distillation via Vision-Language Category Prototype Remember the past: Distilling datasets into addressable memories for neural net- works

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:44:49.987859Z

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Observation bf1874cc-f9b1-472f-a056-7dbad3de3248 · outbound

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

Dataset Distillation via Vision-Language Category Prototype Privacy for free: How does dataset condensation help privacy? In Proceed- ings of the International Conference on Machine Learning (ICML), pages 5378–5396, 2022

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:44:49.783431Z

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Observation e63e1917-c8b5-4fe5-a914-66aafa9a6618 · outbound

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

Dataset Distillation via Vision-Language Category Prototype A survey on dataset distillation: Approaches, applications and future directions

Reference 8

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Observation a1a5d803-abb6-451f-b69d-87c00d0969ca · outbound

This paper cites Efficient dataset distillation via minimax diffusion.

Dataset Distillation via Vision-Language Category Prototype Efficient dataset distillation via minimax diffusion

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:44:49.371253Z

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Observation 1e433c6f-677c-4837-83a3-b8ca47e9372d · outbound

This paper cites A smaller subset of 10 easily classified classes from imagenet, and a little more french, 2020.

Dataset Distillation via Vision-Language Category Prototype A smaller subset of 10 easily classified classes from imagenet, and a little more french, 2020

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:44:49.176022Z

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Observation b86a2804-fe3c-4dc5-8b3e-fa44712c326c · outbound

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

Dataset Distillation via Vision-Language Category Prototype Dataset condensation via efficient synthetic- data parameterization

Reference 11

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Observation 465d9290-e327-4784-8ae9-ee8983444e8b · outbound

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

Dataset Distillation via Vision-Language Category Prototype Learning multiple layers of features from tiny images

Reference 12

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Observation 357be327-c537-4021-99d2-ae01aaf1bd25 · outbound

This paper cites Deep learning.

Dataset Distillation via Vision-Language Category Prototype Deep learning

Reference 13

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Observation bef8a628-f6d9-4bf7-8947-eadf52bd6799 · outbound

This paper cites Dataset condensation with con- trastive signals.

Dataset Distillation via Vision-Language Category Prototype Dataset condensation with con- trastive signals

Reference 14

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Observation 2781580f-83eb-418f-8826-678b849fa84d · outbound

This paper cites A comprehensive survey to dataset distillation.

Dataset Distillation via Vision-Language Category Prototype A comprehensive survey to dataset distillation

Reference 15

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Observation a7b32ad2-772f-4d93-8405-629fd506ad68 · outbound

This paper cites Awesome dataset distillation.

Dataset Distillation via Vision-Language Category Prototype Awesome dataset distillation

Reference 16

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Observation 39cfc7c9-9ef7-4cbe-9937-a16fddffeae1 · outbound

This paper cites Visual instruction tuning.

Dataset Distillation via Vision-Language Category Prototype Visual instruction tuning

Reference 17

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Observation 2fa8bc45-e113-4b12-84b7-9151eeec38b9 · outbound

This paper cites Improved baselines with visual instruction tuning.

Dataset Distillation via Vision-Language Category Prototype Improved baselines with visual instruction tuning

Reference 18

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Observation 168395cb-3785-4218-a9a7-9839f6560cca · outbound

This paper cites Llava-next: Im- proved reasoning, ocr, and world knowledge.

Dataset Distillation via Vision-Language Category Prototype Llava-next: Im- proved reasoning, ocr, and world knowledge

Reference 19

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Observation 3703d49c-9d50-4063-bab6-2b866fb7f083 · outbound

This paper cites Graph Condensation via Receptive Field Distribution Matching.

Dataset Distillation via Vision-Language Category Prototype Graph Condensation via Receptive Field Distribution Matching

Reference 20

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Observation c9bcf66e-695c-44e4-848e-8a71c762399b · outbound

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

Dataset Distillation via Vision-Language Category Prototype The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions

Reference 21

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Observation 36e1a200-d2c2-4e5c-96ea-7c301c116de2 · outbound

This paper cites Dream: Efficient dataset distillation by rep- resentative matching.

Dataset Distillation via Vision-Language Category Prototype Dream: Efficient dataset distillation by rep- resentative matching

Reference 22

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Observation 7ee2d843-63e4-46b8-a070-ea360a854950 · outbound

This paper cites Efficient dataset distillation using random feature ap- proximation.

Dataset Distillation via Vision-Language Category Prototype Efficient dataset distillation using random feature ap- proximation

Reference 23

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Observation 2b71ab99-caf7-46f7-a586-b7533e19e955 · outbound

This paper cites Dataset distillation with convexified implicit gradients.

Dataset Distillation via Vision-Language Category Prototype Dataset distillation with convexified implicit gradients

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:44:47.518495Z

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Observation de5c0835-351b-4879-b778-aff387853819 · outbound

This paper cites Latent dataset distillation with diffusion models.

Dataset Distillation via Vision-Language Category Prototype Latent dataset distillation with diffusion models

Reference 25

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Observation d2fc63b7-f1df-400e-9c5a-ab0ecc6ab42d · outbound

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

Dataset Distillation via Vision-Language Category Prototype Dataset meta-learning from kernel ridge-regression

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:44:47.342879Z

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Observation 3fcaa838-e6da-4829-bb7e-1b720b0b459d · outbound

This paper cites Scalable diffusion models with transformers.

Dataset Distillation via Vision-Language Category Prototype Scalable diffusion models with transformers

Reference 27

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Observation 1fd33f91-5110-4bd5-b1ac-c94e4e16638e · outbound

This paper cites High-resolution image 9 synthesis with latent diffusion models.

Dataset Distillation via Vision-Language Category Prototype High-resolution image 9 synthesis with latent diffusion models

Reference 28

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Observation ca6f22c2-a5b6-424f-8bb7-ccafb6910837 · outbound

This paper cites Data distillation: A survey.

Dataset Distillation via Vision-Language Category Prototype Data distillation: A survey

Reference 29

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Observation 43c6808d-2575-4360-907e-748020bc43e1 · outbound

This paper cites Active learning for convolu- tional neural networks: A core-set approach.

Dataset Distillation via Vision-Language Category Prototype Active learning for convolu- tional neural networks: A core-set approach

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:44:46.692072Z

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Observation c4d560a9-446b-42ca-b826-25a9b6524572 · outbound

This paper cites Dˆ 4: Dataset distillation via disentangled diffusion model.

Dataset Distillation via Vision-Language Category Prototype Dˆ 4: Dataset distillation via disentangled diffusion model

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:44:46.514364Z

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Observation 687a4339-f8bf-4cf4-bb26-8bb319b69052 · outbound

This paper cites Soft-label dataset distillation and text dataset distillation.

Dataset Distillation via Vision-Language Category Prototype Soft-label dataset distillation and text dataset distillation

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:44:46.343423Z

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Observation 24ea05f6-f53a-4c85-98ac-8cdf114bf86b · outbound

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

Dataset Distillation via Vision-Language Category Prototype On the diversity and realism of distilled dataset: An efficient dataset distilla- tion paradigm

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:44:46.166917Z

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Observation 6d81036d-4a66-44f4-9104-c7497644fc79 · outbound

This paper cites Con- trastive multiview coding.

Dataset Distillation via Vision-Language Category Prototype Con- trastive multiview coding

Reference 34

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Observation c61a8ac6-3e57-4724-a754-c5c15a68eee2 · outbound

This paper cites Diffusers: State-of-the-art diffu- sion models.

Dataset Distillation via Vision-Language Category Prototype Diffusers: State-of-the-art diffu- sion models

Reference 35

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Observation ba468aec-e7da-446f-9cee-64d661987111 · outbound

This paper cites Cafe: Learning to condense dataset by align- ing features.

Dataset Distillation via Vision-Language Category Prototype Cafe: Learning to condense dataset by align- ing features

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:44:45.801765Z

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Observation b1628723-2022-4c6d-af60-401ece877a39 · outbound

This paper cites DiM: Distilling Dataset into Generative Model.

Dataset Distillation via Vision-Language Category Prototype DiM: Distilling Dataset into Generative Model

Reference 37

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Source-reported events for the cited work

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Observation 65c00d88-7fc4-42dc-badb-56423f3c973f · outbound

This paper cites Dataset Distillation.

Dataset Distillation via Vision-Language Category Prototype Dataset Distillation

Reference 38

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Observation 48b55beb-6d21-4454-8e15-3d56eab5a328 · outbound

This paper cites Herding dynamical weights to learn.

Dataset Distillation via Vision-Language Category Prototype Herding dynamical weights to learn

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:44:45.650188Z

Source-reported events for the cited work

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Observation bd134fef-4bbb-40af-b934-e401d79263c7 · outbound

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

Dataset Distillation via Vision-Language Category Prototype Squeeze, recover and relabel: Dataset condensation at imagenet scale from a new perspective

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:44:45.507602Z

Source-reported events for the cited work

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Observation df01ba3b-f7ec-40d5-9d57-defc8e09f2cd · outbound

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

Dataset Distillation via Vision-Language Category Prototype Squeeze, recover and relabel: Dataset condensation at imagenet scale from a new perspective

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:44:45.316266Z

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Observation da23ed25-8e36-442a-8bf2-348617392b93 · outbound

This paper cites A compre- hensive survey to dataset distillation.

Dataset Distillation via Vision-Language Category Prototype A compre- hensive survey to dataset distillation

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:44:45.112213Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f2a899de-84fa-44b3-8523-68f280c960d2 · outbound

This paper cites Dataset condensation with dif- ferentiable siamese augmentation.

Dataset Distillation via Vision-Language Category Prototype Dataset condensation with dif- ferentiable siamese augmentation

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:44:44.970040Z

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Observation c8d524a7-a0c5-4625-a795-17ba71484ea9 · outbound

This paper cites Dataset condensation with gra- dient matching.

Dataset Distillation via Vision-Language Category Prototype Dataset condensation with gra- dient matching

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:44:44.823617Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 30b4d1cd-5a96-43cf-bd7a-8c0e3ff14ce6 · outbound

This paper cites Synthesizing informative training samples with gan.

Dataset Distillation via Vision-Language Category Prototype Synthesizing informative training samples with gan

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:44:44.683342Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8aebd75c-4409-445c-a8e3-5d84704d9e23 · outbound

This paper cites Dataset condensation with distri- bution matching.

Dataset Distillation via Vision-Language Category Prototype Dataset condensation with distri- bution matching

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:44:44.544553Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3a4a3ed6-6070-4178-9642-d123f8f94f79 · outbound

This paper cites Im- proved distribution matching for dataset condensation.

Dataset Distillation via Vision-Language Category Prototype Im- proved distribution matching for dataset condensation

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:44:44.375032Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b1a03056-50f2-46b3-9a38-7275dc5d3455 · outbound

This paper cites Dataset distillation using neural feature regression.

Dataset Distillation via Vision-Language Category Prototype Dataset distillation using neural feature regression

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:44:44.209176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Pith citing papers

Observation 33181bcc-9053-40b0-9eac-34737348020c · inbound

Hard Labels In! Rethinking the Role of Hard Labels in Mitigating Local Semantic Drift cites this paper.

Hard Labels In! Rethinking the Role of Hard Labels in Mitigating Local Semantic Drift Dataset Distillation via Vision-Language Category Prototype

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-03T15:50:35.343444Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.