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Paper Citation Record · LEDGER

On the regularization of Wasserstein GANs

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1709.08894.

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

pith.paper-citation-record.v1
1709.08894 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:16:01.202955Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T06:16:28.064256Z

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 63587cc5-733a-4eaf-b128-a4515e8373c5 · inbound

Computing Optimal Transport Maps and Wasserstein Barycenters Using Conditional Normalizing Flows cites this paper.

Computing Optimal Transport Maps and Wasserstein Barycenters Using Conditional Normalizing Flows On the regularization of Wasserstein GANs

Reference 2021

Resolution
unresolved
no resolver link, observed 2026-08-07T13:16:01.202955Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:16:01.202955Z digest=sha256:ce6453b9d20d49dedf6dfc9f868b873cb2ac54733a970174c757f9d87332e253

Observation 6be04830-6baa-4ee8-b8a6-6b5181183679 · inbound

What is the Cost of Differential Privacy for Deep Learning-Based Trajectory Generation? cites this paper.

What is the Cost of Differential Privacy for Deep Learning-Based Trajectory Generation? On the regularization of Wasserstein GANs

Reference 46

Resolution
verified exact
local_arxiv, observed 2026-08-07T05:03:52.773398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T05:03:51.469006Z digest=sha256:3a9c78fcdffa4014f801b761bce9da7b53a19252c58df758c58c767d1dfb85df