Paper Citation Record · LEDGER

DiM: Distilling Dataset into Generative Model

As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 15 inbound Pith citation observations for arXiv:2303.04707.

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

pith.paper-citation-record.v1
2303.04707 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 15 of 15 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 15 of 15 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T10:53:14.994867Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-20T12:23:17.009380Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 5706af70-ebab-444c-a03f-ceba29890ab3 · inbound

Color-Oriented Redundancy Reduction in Dataset Distillation cites this paper.

Color-Oriented Redundancy Reduction in Dataset Distillation DiM: Distilling Dataset into Generative Model

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-12T18:44:38.980282Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2daaf3f4-5d80-4188-aaf8-8db254538cab · inbound

Data-to-Model Distillation: Data-Efficient Learning Framework cites this paper.

Data-to-Model Distillation: Data-Efficient Learning Framework DiM: Distilling Dataset into Generative Model

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-12T17:14:28.347680Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bd120e02-c815-4c02-b9a8-d5b086310853 · inbound

Generative Dataset Distillation Based on Self-knowledge Distillation cites this paper.

Generative Dataset Distillation Based on Self-knowledge Distillation DiM: Distilling Dataset into Generative Model

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-10T21:43:09.581396Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b2bae93d-e731-49ae-9133-0fb1d2a5ef22 · inbound

Unifying Dataset Pruning and Distillation for Efficient Large-scale Compression cites this paper.

Unifying Dataset Pruning and Distillation for Efficient Large-scale Compression DiM: Distilling Dataset into Generative Model

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-08T15:32:04.803792Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 89d13229-b2c7-433b-909a-4ada3e2ce53a · inbound

Latent Video Dataset Distillation cites this paper.

Latent Video Dataset Distillation DiM: Distilling Dataset into Generative Model

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-16T10:53:14.994867Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bf5b998d-83b8-49c8-a3f2-2c62ef95214f · inbound

CONCORD: Concept-Informed Diffusion for Dataset Distillation cites this paper.

CONCORD: Concept-Informed Diffusion for Dataset Distillation DiM: Distilling Dataset into Generative Model

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-07T14:35:45.876191Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b034bd72-416d-4591-b7d5-52cb7957da36 · inbound

MGD$^3$: Mode-Guided Dataset Distillation using Diffusion Models cites this paper.

MGD$^3$: Mode-Guided Dataset Distillation using Diffusion Models DiM: Distilling Dataset into Generative Model

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-07T14:26:58.083371Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 283ebcac-cb1b-46a8-bfa4-46fe5be3d6c8 · inbound

Diversity-Driven Generative Dataset Distillation Based on Diffusion Model with Self-Adaptive Memory cites this paper.

Diversity-Driven Generative Dataset Distillation Based on Diffusion Model with Self-Adaptive Memory DiM: Distilling Dataset into Generative Model

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T14:17:43.752081Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 46df52c5-15e6-42ce-924d-2d15d0b7f261 · inbound

Dynamic-Aware Video Distillation: Optimizing Temporal Resolution Based on Video Semantics cites this paper.

Dynamic-Aware Video Distillation: Optimizing Temporal Resolution Based on Video Semantics DiM: Distilling Dataset into Generative Model

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-07T13:16:15.789920Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

Dataset Distillation via Vision-Language Category Prototype cites this paper.

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

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-06T21:44:42.157743Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ef767539-f37c-4594-b9f4-5db9935e5cbe · inbound

Diffusion Models as Dataset Distillation Priors cites this paper.

Diffusion Models as Dataset Distillation Priors DiM: Distilling Dataset into Generative Model

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-18T06:05:57.414305Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 1234adc2-e4dc-4b5b-8e7a-1d1a60fc70c5 · inbound

SAS: Semantic-aware Sampling for Generative Dataset Distillation cites this paper.

SAS: Semantic-aware Sampling for Generative Dataset Distillation DiM: Distilling Dataset into Generative Model

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-20T12:23:17.011033Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 2eb0d057-8e57-4e71-a991-97ee812ae779 · inbound

Dataset Distillation by Influence Matching cites this paper.

Dataset Distillation by Influence Matching DiM: Distilling Dataset into Generative Model

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-01T19:49:26.598542Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 370288a5-defb-4954-8208-7e7764f89c71 · inbound

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

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

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-01T02:50:18.105606Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.