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Paper Citation Record · LEDGER

HyperGAN-CLIP: A Unified Framework for Domain Adaptation, Image Synthesis and Manipulation

As of 18 August 2026, this Paper Citation Record lists 10 of 10 outbound references and 0 inbound Pith citation observations for arXiv:2411.12832.

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

pith.paper-citation-record.v1
2411.12832 v1

Coverage vector

measured 10 of 10 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T17:13:28.973973Z

measured 10 of 10 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+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

10 of 10 outbound references displayed

  • verified exact0
  • verified fuzzy6
  • unresolved4
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 08186345-f89f-48bc-9e95-bda0157cb9c9 · outbound

This paper cites IEEE Transactions on Pattern Analysis and Machine Intelligence44, 10 (oct 2022), 5962–5979.

HyperGAN-CLIP: A Unified Framework for Domain Adaptation, Image Synthesis and Manipulation IEEE Transactions on Pattern Analysis and Machine Intelligence44, 10 (oct 2022), 5962–5979

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-12T17:13:28.722822Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T17:13:28.722822Z digest=sha256:50769c527bf3c1fa0d050bc931d70aa6b4692d3e85976de2a1fa9d9c755ca032

Observation d47c14d2-92fa-4fb7-a191-00eb88170327 · outbound

This paper cites ACM Trans.

HyperGAN-CLIP: A Unified Framework for Domain Adaptation, Image Synthesis and Manipulation ACM Trans

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T17:13:29.604730Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-12T17:13:28.745187Z digest=sha256:afcbf4943779ab238f7893159ca4e584cbd845ee4bf326737b8b646c1a76f591

Observation 2721e7c3-8142-43f4-836b-f202b1ebe5fb · outbound

This paper cites Plug-and-Play Diffusion Features for Text-Driven Image-to-Image Translation.

HyperGAN-CLIP: A Unified Framework for Domain Adaptation, Image Synthesis and Manipulation Plug-and-Play Diffusion Features for Text-Driven Image-to-Image Translation

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-12T17:13:28.865656Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T17:13:28.865656Z digest=sha256:fe90d836c8df73f935ccc21dceb50f198aab5c98ea486bbd317b64210bdb6d61

Observation 78621bc1-64b4-4254-9a75-d4b9f1357e45 · outbound

This paper cites Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2022).

HyperGAN-CLIP: A Unified Framework for Domain Adaptation, Image Synthesis and Manipulation Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2022)

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T17:13:29.425173Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-12T17:13:28.929185Z digest=sha256:daf6264e3f5fd364d12574680b3a3ba569d11b567f80e5ca880992a4615bbd82

Observation 58d94252-4fcd-4005-9892-10de0c2fc929 · outbound

This paper cites The comparisons on a smaller set of domains shows that our proposed HyperGAN- CLIP model performs comparably or better than HyperDomainNet.

HyperGAN-CLIP: A Unified Framework for Domain Adaptation, Image Synthesis and Manipulation The comparisons on a smaller set of domains shows that our proposed HyperGAN- CLIP model performs comparably or better than HyperDomainNet

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T17:13:29.364191Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-12T17:13:28.973973Z digest=sha256:2b567ad1a45c15b400773cf0d45acff7ee8650a4a7f04634b0a82d0b009acab8

Observation 6c123903-5b1e-4b2e-865e-87f359a489e3 · outbound

This paper cites Deep Residual Learning for Image Recognition.

HyperGAN-CLIP: A Unified Framework for Domain Adaptation, Image Synthesis and Manipulation Deep Residual Learning for Image Recognition

Reference 2015

Resolution
unresolved
no resolver link, observed 2026-08-12T17:13:28.774527Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T17:13:28.774527Z digest=sha256:ba710a7024deebd3d285e97bda6bc0fa7fee731df5bc5ede7aac353cec4f71d0

Observation 2b8eb133-1f16-4b47-856c-9f062414932e · outbound

This paper cites Improved Baselines with Momentum Contrastive Learning.

HyperGAN-CLIP: A Unified Framework for Domain Adaptation, Image Synthesis and Manipulation Improved Baselines with Momentum Contrastive Learning

Reference 2020

Resolution
unresolved
no resolver link, observed 2026-08-12T17:13:28.687596Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T17:13:28.687596Z digest=sha256:e1d1620fdd1e4b5244f4fa3f893820262f70128504b05c45dd9b6e985c2e2d11

Observation e36c3412-8f98-4d60-9810-9aab8b6ed373 · outbound

This paper cites InProceedings of the IEEE/CVF International Conference on Computer Vision (ICCV).

HyperGAN-CLIP: A Unified Framework for Domain Adaptation, Image Synthesis and Manipulation InProceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)

Reference 2021

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T17:13:29.497134Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-12T17:13:28.823367Z digest=sha256:4682a2cbb403d945b7322fc40075601ffb2789cbc3305ae485d03e7b9c73e1d1

Observation 94994874-a5f6-4fc9-8680-7d6addee431d · outbound

This paper cites In Computer Vision – ECCV 2022: 17th European Conference, Tel A viv, Israel, October 23–27, 2022, Proceedings, Part XIII (Tel Aviv, Israel).

HyperGAN-CLIP: A Unified Framework for Domain Adaptation, Image Synthesis and Manipulation In Computer Vision – ECCV 2022: 17th European Conference, Tel A viv, Israel, October 23–27, 2022, Proceedings, Part XIII (Tel Aviv, Israel)

Reference 2022

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T17:13:29.678823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-12T17:13:28.650512Z digest=sha256:7133738d95edfe0d41782605659c04112e1f1236366f2afdd90ee7bd5f3f5e0a

Observation a7488f3c-23e4-41cd-82cd-29aec8c41b12 · outbound

This paper cites ACM Trans.

HyperGAN-CLIP: A Unified Framework for Domain Adaptation, Image Synthesis and Manipulation ACM Trans

Reference 2023

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T17:13:29.758188Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-12T17:13:28.598570Z digest=sha256:7312e36fa4db3386af5ed1a84b9f41eb8a2b30644dc7fd16d4a105f6101b3b9c

Pith citing papers

No inbound Pith citation observations are available.