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Paper Citation Record · LEDGER

DisDiff: Unsupervised Disentanglement of Diffusion Probabilistic Models

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2301.13721.

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

pith.paper-citation-record.v1
2301.13721 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 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 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:37:29.328834Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-07T18:04:00.617658Z

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 8b3cd198-c8de-467d-b2ae-e77729243606 · inbound

Enhancing Interpretability of Sparse Latent Representations with Class Information cites this paper.

Enhancing Interpretability of Sparse Latent Representations with Class Information DisDiff: Unsupervised Disentanglement of Diffusion Probabilistic Models

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-07T15:37:29.328834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T15:37:29.328834Z digest=sha256:e0e510d4428b9ff1919bd82db236603a47d1639cda2ffcb8e8f73dbd5a347f13

Observation 0971d5ec-d64d-4c0a-9b09-743624b014cc · inbound

Diffusion Counterfactual Generation with Semantic Abduction cites this paper.

Diffusion Counterfactual Generation with Semantic Abduction DisDiff: Unsupervised Disentanglement of Diffusion Probabilistic Models

Reference 142

Resolution
unresolved
no resolver link, observed 2026-08-07T05:30:33.209795Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T05:30:33.209795Z digest=sha256:591866499d0582fc15b3fd7475e9f96582a0f30bb9881318c2818d6f63c3d481

Observation 2702fef2-ca23-452c-b9b1-28eb39d806c0 · inbound

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning cites this paper.

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning DisDiff: Unsupervised Disentanglement of Diffusion Probabilistic Models

Reference 58

Resolution
unresolved
no resolver link, observed 2026-08-06T18:10:15.836359Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:10:15.836359Z digest=sha256:cf0d9b5d85889aa51bc17705326a11c434d901c284d80f964555f70bcf5cb507

Observation dc7a9cd8-ea1e-4185-9196-b99e346f67d5 · inbound

Steering Optimisation Trajectories in Diffusion Representation Learning cites this paper.

Steering Optimisation Trajectories in Diffusion Representation Learning DisDiff: Unsupervised Disentanglement of Diffusion Probabilistic Models

Reference 88

Resolution
verified exact
local_arxiv, observed 2026-07-07T18:04:00.619182Z

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.

source=pdf_text observed=2026-07-07T17:54:09.140773Z digest=sha256:d78885fbc67801d88d816e292fcf7988cae3c974a404e86e0e7163c04aaebf74

Observation 237a5efd-b129-4827-b501-253c12480082 · inbound

Decafs: Disentangled Conditional adversarial Flows cites this paper.

Decafs: Disentangled Conditional adversarial Flows DisDiff: Unsupervised Disentanglement of Diffusion Probabilistic Models

Reference 2019

Resolution
unresolved
no resolver link, observed 2026-08-01T14:34:32.000533Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T14:34:32.000533Z digest=sha256:3acf4499f4c7e259949b5e2a8331964c7620a12385554a5ae2cae4b7921f43a1

Observation 26ba65bb-8423-4ddd-8097-01291e6ffc88 · inbound

Riemannian Deep Learning: Modules, Networks, and Geometries cites this paper.

Riemannian Deep Learning: Modules, Networks, and Geometries DisDiff: Unsupervised Disentanglement of Diffusion Probabilistic Models

Reference 194

Resolution
unresolved
no resolver link, observed 2026-08-01T12:55:30.582963Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T12:55:30.582963Z digest=sha256:ad350e634561840507b490df7775c4869ab1650433fcdc87925bd47268c7ca91