Paper Citation Record · LEDGER

Fully Automated Image De-fencing using Conditional Generative Adversarial Networks

As of 17 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:1908.06837.

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

pith.paper-citation-record.v1
1908.06837 v1

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T12:37:56.040478Z

measured 41 of 41 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

41 of 41 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c72e1854-6389-4116-b836-b465f649f669 · outbound

This paper cites Simultaneous structure and texture image inpainting.

Fully Automated Image De-fencing using Conditional Generative Adversarial Networks Simultaneous structure and texture image inpainting

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:37:56.579229Z

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.

Observation f6ea79a7-80f3-4f91-a5e1-7376b532a600 · outbound

This paper cites I know that person: Generative full body and face de-identification of people in images.

Fully Automated Image De-fencing using Conditional Generative Adversarial Networks I know that person: Generative full body and face de-identification of people in images

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:37:56.566512Z

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

Observation bc60bc8f-ef54-43e3-a178-aa9bdcfd654b · outbound

This paper cites Infogan: Interpretable representation learning by information maximizing generative adversarial nets.

Fully Automated Image De-fencing using Conditional Generative Adversarial Networks Infogan: Interpretable representation learning by information maximizing generative adversarial nets

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:37:56.553868Z

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.

Observation a57fd649-4e9e-42d3-857d-a9b594b5af53 · outbound

This paper cites Region filling and object removal by exemplar-based image inpainting.

Fully Automated Image De-fencing using Conditional Generative Adversarial Networks Region filling and object removal by exemplar-based image inpainting

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:37:56.541381Z

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.

Observation e304cc4c-5ee7-4dbf-bf2d-a81fa70844c4 · outbound

This paper cites Image melding: Combining inconsistent images using patch-based synthesis.

Fully Automated Image De-fencing using Conditional Generative Adversarial Networks Image melding: Combining inconsistent images using patch-based synthesis

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:37:56.529103Z

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.

Observation e8b05966-7df4-4eb0-87d5-cfe100ee421f · outbound

This paper cites Accurate and efficient video de-fencing using convolutional neural networks and temporal information.

Fully Automated Image De-fencing using Conditional Generative Adversarial Networks Accurate and efficient video de-fencing using convolutional neural networks and temporal information

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:37:56.516182Z

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.

Observation 717157ee-e6de-4bbd-b8a3-37cc4d6b521e · outbound

This paper cites The pascal visual object classes (voc) challenge.

Fully Automated Image De-fencing using Conditional Generative Adversarial Networks The pascal visual object classes (voc) challenge

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:37:56.502764Z

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.

Observation fe138e65-5eb8-43f6-adc3-017087fe4bcb · outbound

This paper cites Image de-fencing framework with hybrid inpainting algorithm.

Fully Automated Image De-fencing using Conditional Generative Adversarial Networks Image de-fencing framework with hybrid inpainting algorithm

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:37:56.488449Z

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.

Observation 2c219390-8756-46b9-8ee8-45c9d290bc75 · outbound

This paper cites Efficient belief propagation for early vision.

Fully Automated Image De-fencing using Conditional Generative Adversarial Networks Efficient belief propagation for early vision

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:37:56.474961Z

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.

Observation a589498e-d607-41d4-8893-bf82e1744f72 · outbound

This paper cites Image style transfer using convolutional neural networks.

Fully Automated Image De-fencing using Conditional Generative Adversarial Networks Image style transfer using convolutional neural networks

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:37:56.461616Z

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.

Observation 083a5a39-b799-449d-b7de-648c64d23fb9 · outbound

This paper cites Discovering texture regularity as a higher-order correspondence problem.

Fully Automated Image De-fencing using Conditional Generative Adversarial Networks Discovering texture regularity as a higher-order correspondence problem

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:37:56.447655Z

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.

Observation 19d7890b-459b-4226-b1aa-951abef82a70 · outbound

This paper cites An Introduction to Image Synthesis with Generative Adversarial Nets.

Fully Automated Image De-fencing using Conditional Generative Adversarial Networks An Introduction to Image Synthesis with Generative Adversarial Nets

Reference 12

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Source-reported events for the cited work

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Observation 82dffe07-382e-48f0-b77d-be8985a1ba76 · outbound

This paper cites Image completion using planar structure guidance.

Fully Automated Image De-fencing using Conditional Generative Adversarial Networks Image completion using planar structure guidance

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:37:56.434386Z

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.

Observation 035612eb-7c55-464b-aee4-c57425c8a2d2 · outbound

This paper cites Image-to-image translation with conditional adversarial networks.

Fully Automated Image De-fencing using Conditional Generative Adversarial Networks Image-to-image translation with conditional adversarial networks

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-14T12:37:55.933757Z

Source-reported events for the cited work

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Observation d3f12488-e689-4b13-834d-221d16b29ce6 · outbound

This paper cites Perceptual losses for real-time style transfer and super-resolution.

Fully Automated Image De-fencing using Conditional Generative Adversarial Networks Perceptual losses for real-time style transfer and super-resolution

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-14T12:37:55.937458Z

Source-reported events for the cited work

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Observation ffe5014d-f806-43b2-bb24-5cc817587a60 · outbound

This paper cites My camera can see through fences: A deep learning approach for image de-fencing.

Fully Automated Image De-fencing using Conditional Generative Adversarial Networks My camera can see through fences: A deep learning approach for image de-fencing

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:37:56.404318Z

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.

Observation ac84b8d8-6567-4e58-9281-ed2988c76867 · outbound

This paper cites A multimodal approach for image de-fencing and depth inpainting.

Fully Automated Image De-fencing using Conditional Generative Adversarial Networks A multimodal approach for image de-fencing and depth inpainting

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:37:56.391201Z

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.

Observation 250d130b-5503-4608-993c-4df3738d1090 · outbound

This paper cites Deep learning based fence segmentation and removal from an image using a video sequence.

Fully Automated Image De-fencing using Conditional Generative Adversarial Networks Deep learning based fence segmentation and removal from an image using a video sequence

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:37:56.379696Z

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.

Observation e48c6675-3eb6-4936-9e92-6a78eaab2509 · outbound

This paper cites Seeing through the fence: Image de-fencing using a video sequence.

Fully Automated Image De-fencing using Conditional Generative Adversarial Networks Seeing through the fence: Image de-fencing using a video sequence

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:37:56.367717Z

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.

Observation 1064ab33-4ece-4b53-8b4e-e219259ee438 · outbound

This paper cites Image defencing via signal demixing.

Fully Automated Image De-fencing using Conditional Generative Adversarial Networks Image defencing via signal demixing

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:37:56.353494Z

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.

Observation 41a68a56-62ec-4561-9f60-f84bcae757aa · outbound

This paper cites A closed-form solution to natural image matting.

Fully Automated Image De-fencing using Conditional Generative Adversarial Networks A closed-form solution to natural image matting

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:37:56.340894Z

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.

Observation fc9841d2-971a-4aaf-8ffc-e76ae9e941ff · outbound

This paper cites Microsoft coco: Common objects in context.

Fully Automated Image De-fencing using Conditional Generative Adversarial Networks Microsoft coco: Common objects in context

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:37:56.327797Z

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.

Observation f1ddba5b-64f3-4396-bb03-eb386ee0979b · outbound

This paper cites A lattice-based mrf model for dynamic near-regular texture tracking.

Fully Automated Image De-fencing using Conditional Generative Adversarial Networks A lattice-based mrf model for dynamic near-regular texture tracking

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:37:56.314581Z

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.

Observation fcb848df-eb04-47fe-8bba-072c7dc9ddce · outbound

This paper cites Image de-fencing.

Fully Automated Image De-fencing using Conditional Generative Adversarial Networks Image de-fencing

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:37:56.301734Z

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.

Observation 20f18e5f-3323-4ed2-b346-28a3c0227909 · outbound

This paper cites Fully convolutional networks for semantic segmentation.

Fully Automated Image De-fencing using Conditional Generative Adversarial Networks Fully convolutional networks for semantic segmentation

Reference 25

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Source-reported events for the cited work

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Observation 6e698a84-1f1a-4c4d-934d-11dd7ab1f3ec · outbound

This paper cites Video de-fencing.

Fully Automated Image De-fencing using Conditional Generative Adversarial Networks Video de-fencing

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:37:56.280797Z

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.

Observation f1a73f22-8c6d-44f4-8944-b49bad7e806d · outbound

This paper cites EdgeConnect: Generative Image Inpainting with Adversarial Edge Learning.

Fully Automated Image De-fencing using Conditional Generative Adversarial Networks EdgeConnect: Generative Image Inpainting with Adversarial Edge Learning

Reference 27

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Source-reported events for the cited work

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Observation 655f6d1d-9dc1-4f12-b94a-e7c70b6ef243 · outbound

This paper cites Deformed lattice discovery via efficient mean-shift belief propagation.

Fully Automated Image De-fencing using Conditional Generative Adversarial Networks Deformed lattice discovery via efficient mean-shift belief propagation

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:37:56.268870Z

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.

Observation 3be53973-f5f7-4a39-b82a-efa880f5b3a9 · outbound

This paper cites Deformed lattice detection in real-world images using mean-shift belief propagation.

Fully Automated Image De-fencing using Conditional Generative Adversarial Networks Deformed lattice detection in real-world images using mean-shift belief propagation

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:37:56.256643Z

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.

Observation 4230bb42-eb8e-4368-b2ac-ec140641dc37 · outbound

This paper cites Image de-fencing revisited.

Fully Automated Image De-fencing using Conditional Generative Adversarial Networks Image de-fencing revisited

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:37:56.244319Z

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.

Observation b3985ed4-1ebd-4e45-9772-9031dab0deca · outbound

This paper cites Context encoders: Feature learning by inpainting.

Fully Automated Image De-fencing using Conditional Generative Adversarial Networks Context encoders: Feature learning by inpainting

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:37:56.231454Z

Source-reported events for the cited work

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Observation 069828cd-d132-4af9-93b3-8fd29db7865f · outbound

This paper cites Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks.

Fully Automated Image De-fencing using Conditional Generative Adversarial Networks Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

Reference 32

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Source-reported events for the cited work

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Observation 240db16a-c224-4f16-88fc-877d387ece55 · outbound

This paper cites Generative Adversarial Text to Image Synthesis.

Fully Automated Image De-fencing using Conditional Generative Adversarial Networks Generative Adversarial Text to Image Synthesis

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-14T12:37:56.007656Z

Source-reported events for the cited work

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Observation f50c1d89-0e20-4e40-b7a4-936a013f14e5 · outbound

This paper cites Image quality assessment: from error visibility to structural similarity.

Fully Automated Image De-fencing using Conditional Generative Adversarial Networks Image quality assessment: from error visibility to structural similarity

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:37:56.218503Z

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.

Observation 71e31fd2-595d-471b-8f04-95291e55ef53 · outbound

This paper cites Image inpainting by patch propagation using patch sparsity.

Fully Automated Image De-fencing using Conditional Generative Adversarial Networks Image inpainting by patch propagation using patch sparsity

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:37:56.205771Z

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.

Observation 2b3f5a29-472b-438d-986a-2f2746e3a14d · outbound

This paper cites High-resolution image inpainting using multi-scale neural patch synthesis.

Fully Automated Image De-fencing using Conditional Generative Adversarial Networks High-resolution image inpainting using multi-scale neural patch synthesis

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:37:56.191864Z

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.

Observation 02421af6-3238-4485-8ccd-6cb453e25a85 · outbound

This paper cites Semantic image inpainting with deep generative models.

Fully Automated Image De-fencing using Conditional Generative Adversarial Networks Semantic image inpainting with deep generative models

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:37:56.178029Z

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.

Observation de179298-560c-4a4d-8c34-8dfe6384942d · outbound

This paper cites Lin, Jimei Yang, Xiaohui Shen, Xin Lu, and Thomas S.

Fully Automated Image De-fencing using Conditional Generative Adversarial Networks Lin, Jimei Yang, Xiaohui Shen, Xin Lu, and Thomas S

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:37:56.165023Z

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.

Observation c46f8538-5bb7-430f-9050-7a9e24970778 · outbound

This paper cites Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks.

Fully Automated Image De-fencing using Conditional Generative Adversarial Networks Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:37:56.152787Z

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.

Observation c2c191ba-1b4c-4197-bf5f-15ad1a8d87bd · outbound

This paper cites Loss functions for image restoration with neural networks.

Fully Automated Image De-fencing using Conditional Generative Adversarial Networks Loss functions for image restoration with neural networks

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:37:56.141079Z

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.

Observation dd16e625-2583-4a05-a231-b599f3a376f9 · outbound

This paper cites Learning based digital mmtting.

Fully Automated Image De-fencing using Conditional Generative Adversarial Networks Learning based digital mmtting

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:37:56.127709Z

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.

Pith citing papers

No inbound Pith citation observations are available.