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Paper Citation Record · LEDGER

RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution

As of 19 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:1908.06382.

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

pith.paper-citation-record.v1
1908.06382 v2

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T12:51:03.350275Z

measured 43 of 43 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+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

43 of 43 outbound references displayed

  • verified exact3
  • verified fuzzy33
  • unresolved7
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 00b5fbac-ecf4-474c-8433-aef62c16465e · outbound

This paper cites Ntire 2017 challenge on single image super-resolution: Dataset and study.

RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution Ntire 2017 challenge on single image super-resolution: Dataset and study

Reference 1

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source=arxiv_source observed=2026-08-14T12:51:03.226761Z digest=sha256:be925c5724bf0f79e95d395be7f91bab1d541599cb06f38ff3cb2a926f864418

Observation aae0e70d-2328-46bb-83be-de425e87bf65 · outbound

This paper cites The 2018 PIRM Challenge on Perceptual Image Super-resolution.

RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution The 2018 PIRM Challenge on Perceptual Image Super-resolution

Reference 2

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source=arxiv_source observed=2026-08-14T12:51:03.230104Z digest=sha256:d7e2c48923be7e2cda77cf44e354292c80a019ce892efb0243047aec09cfa5af

Observation 62c7c786-5a77-462c-81af-88720065c9ee · outbound

This paper cites The perception-distortion tradeoff.

RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution The perception-distortion tradeoff

Reference 3

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Observation 176d4cfb-6458-41ae-a7c1-e75962f8ec03 · outbound

This paper cites Deep neural networks for no-reference and full-reference image quality assessment.

RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution Deep neural networks for no-reference and full-reference image quality assessment

Reference 4

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source=arxiv_source observed=2026-08-14T12:51:03.236683Z digest=sha256:796aece50ff6d8103064a7d26c9ce7e62615e22ff387d395d367f4f73b6d73c1

Observation 2448dbe8-1792-4212-a423-342c87181f0f · outbound

This paper cites a ckinger, and Roopak Shah. Signature verification using a.

RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution a ckinger, and Roopak Shah. Signature verification using a

Reference 5

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Observation 4319e4b4-4c1a-4ef2-8b0f-c524f1666576 · outbound

This paper cites Super-Resolution with Deep Convolutional Sufficient Statistics.

RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution Super-Resolution with Deep Convolutional Sufficient Statistics

Reference 6

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source=arxiv_source observed=2026-08-14T12:51:03.242837Z digest=sha256:fcf0710f16b084c0cc179802d98a708cc17e7ca4183d9fee4eb72293d60ce453

Observation efd30582-06dd-4147-a32c-f85f16ab0a97 · outbound

This paper cites Learning to rank using gradient descent.

RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution Learning to rank using gradient descent

Reference 7

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source=arxiv_source observed=2026-08-14T12:51:03.246082Z digest=sha256:ab94d53572b35c3e1af68721fc54ac67187f8e2913df67a56c22a7bd99edd0ed

Observation 4f273fab-c564-4157-ad77-9a4957f0b364 · outbound

This paper cites Deep Learning-based Image Super-Resolution Considering Quantitative and Perceptual Quality.

RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution Deep Learning-based Image Super-Resolution Considering Quantitative and Perceptual Quality

Reference 8

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Observation 75d0f776-6450-4d9c-a5cc-95af0e14849e · outbound

This paper cites Learning a similarity metric discriminatively, with application to face verification.

RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution Learning a similarity metric discriminatively, with application to face verification

Reference 9

Resolution
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source=arxiv_source observed=2026-08-14T12:51:03.251767Z digest=sha256:32686fdd9994de7b50f2eedd52c1ae16701493df6ca31de207ae9cc1b04a7c87

Observation d367f756-5d3a-4541-8dea-06200d13128b · outbound

This paper cites Learning a deep convolutional network for image super-resolution.

RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution Learning a deep convolutional network for image super-resolution

Reference 10

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source=arxiv_source observed=2026-08-14T12:51:03.254239Z digest=sha256:7702353faa0429340b3371a20f00a10bee8d86cb30d968dad778f793ae76edec

Observation 7a11cee7-c6ff-40a6-b9c7-d95a2c7ad887 · outbound

This paper cites Accelerating the super-resolution convolutional neural network.

RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution Accelerating the super-resolution convolutional neural network

Reference 11

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Observation b0ab1e39-7793-4dbe-863b-e792a956fa7b · outbound

This paper cites Generating images with perceptual similarity metrics based on deep networks.

RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution Generating images with perceptual similarity metrics based on deep networks

Reference 12

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Observation ade4306d-d8ee-4ab9-88f9-a87477cfc406 · outbound

This paper cites Suppressing model overfitting for image super-resolution networks.

RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution Suppressing model overfitting for image super-resolution networks

Reference 13

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Observation 4669d498-7011-4b55-82c4-d033bd534f2f · outbound

This paper cites Blind super-resolution with iterative kernel correction.

RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution Blind super-resolution with iterative kernel correction

Reference 14

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source=arxiv_source observed=2026-08-14T12:51:03.266450Z digest=sha256:181a1c7fb987619b873ec1ceef3898154c9f47a1ffb7e2a5fc25d800bfeb3271

Observation 41fd68f7-f8ee-4f27-83e1-d0b7122128b6 · outbound

This paper cites Deep backprojection networks for super-resolution.

RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution Deep backprojection networks for super-resolution

Reference 15

Resolution
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raw_fallback, observed 2026-08-14T12:51:03.715900Z

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Observation ef0ecec8-b34c-4082-9078-923eab682c7f · outbound

This paper cites Modulating image restoration with continual levels via adaptive feature modification layers.

RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution Modulating image restoration with continual levels via adaptive feature modification layers

Reference 16

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source=arxiv_source observed=2026-08-14T12:51:03.271920Z digest=sha256:44ecae19e8b71953c371b00feffe550a56d98a23489c3c7b72fad5e2bec81649

Observation 543957b7-ba2a-4f4b-923d-494f3547dfa8 · outbound

This paper cites Delving deep into rectifiers: Surpassing human-level performance on imagenet classification.

RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution Delving deep into rectifiers: Surpassing human-level performance on imagenet classification

Reference 17

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Observation c802cdac-8350-4c54-b973-45d4df6210af · outbound

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

RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution Perceptual losses for real-time style transfer and super-resolution

Reference 18

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Observation a623131b-7c4d-4154-8ede-5634e85f847f · outbound

This paper cites Convolutional neural networks for no-reference image quality assessment.

RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution Convolutional neural networks for no-reference image quality assessment

Reference 19

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Observation 055b0bcb-bf4c-456b-916c-665619288196 · outbound

This paper cites Accurate image super-resolution using very deep convolutional networks.

RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution Accurate image super-resolution using very deep convolutional networks

Reference 20

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Observation ae1f7ece-5d0b-4641-b7b2-5904950a8126 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution Adam: A Method for Stochastic Optimization

Reference 21

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source=arxiv_source observed=2026-08-14T12:51:03.285770Z digest=sha256:b9166f186fc27619e512a32ecfdb7fdef80a1692492527bd5fdd92aa2b5349b1

Observation 9defb464-ea74-4625-a36f-f925d04d5e36 · outbound

This paper cites Photo-realistic single image super-resolution using a generative adversarial network.

RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution Photo-realistic single image super-resolution using a generative adversarial network

Reference 22

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Observation f1808959-f6da-4135-afd7-0bbfd5c44a18 · outbound

This paper cites Blind image quality assessment using a general regression neural network.

RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution Blind image quality assessment using a general regression neural network

Reference 23

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raw_fallback, observed 2026-08-14T12:51:03.664678Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-14T12:51:03.291345Z digest=sha256:1fb7a4fd61961f19403dd2bb8ec91557b713d14be236497a8dfac2e935aa459a

Observation 4b5a1bfc-bed0-443a-b3fd-8dfb66e9812c · outbound

This paper cites Enhanced deep residual networks for single image super-resolution.

RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution Enhanced deep residual networks for single image super-resolution

Reference 24

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source=arxiv_source observed=2026-08-14T12:51:03.293649Z digest=sha256:897d02429233619ff82d208eaea803c7669355a21d8c7e76d8572ecff1f7c25a

Observation a281ff9b-09ed-46ab-9385-b541afee4fbf · outbound

This paper cites RankIQA: Learning from Rankings for No-reference Image Quality Assessment.

RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution RankIQA: Learning from Rankings for No-reference Image Quality Assessment

Reference 25

Resolution
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local_arxiv, observed 2026-08-14T12:51:03.503854Z

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Observation f02cff74-dfbd-4fa5-b658-24e690349e87 · outbound

This paper cites Learning a no-reference quality metric for single-image super-resolution.

RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution Learning a no-reference quality metric for single-image super-resolution

Reference 26

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source=arxiv_source observed=2026-08-14T12:51:03.299811Z digest=sha256:04343f4bb2e2098546f23a0ca00a3db4e48d64f735fbf955a52fd746e949c579

Observation 60356e95-3fa1-4d95-b8b1-004820fbbbac · outbound

This paper cites A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics.

RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T12:51:03.640438Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 8202f1d1-82e7-43df-9259-53592d6a77c6 · outbound

This paper cites Conditional Generative Adversarial Nets.

RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution Conditional Generative Adversarial Nets

Reference 28

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Observation f7eb8ccb-4442-4a20-9d0a-c3aa35186aff · outbound

This paper cites completely blind.

RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution completely blind

Reference 29

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source=arxiv_source observed=2026-08-14T12:51:03.309631Z digest=sha256:72e9fb3e6347d639a09b9242a110dbb1951a53818d2e47fc8b6aaa6c9cc77fa6

Observation 4efe0e1c-0b2d-46af-9c71-e873543059d2 · outbound

This paper cites Relative attributes.

RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution Relative attributes

Reference 30

Resolution
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raw_fallback, observed 2026-08-14T12:51:03.626846Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-14T12:51:03.312388Z digest=sha256:2cc724e8577115926c67da5917fc93e5f299166aaa87fa5d8d415773ef7fe7c2

Observation cf390818-5260-4333-a7cd-b7b40e9129d5 · outbound

This paper cites Enhancenet: Single image super-resolution through automated texture synthesis.

RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution Enhancenet: Single image super-resolution through automated texture synthesis

Reference 31

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T12:51:03.619219Z

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source=arxiv_source observed=2026-08-14T12:51:03.316017Z digest=sha256:6cd1409882ebd3591b65cc294bcb29fb0541a21661d8e20712b3c48187376e38

Observation 87251259-cddf-49f4-9b47-76e3b2495295 · outbound

This paper cites Ranking cgans: Subjective control over semantic image attributes.

RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution Ranking cgans: Subjective control over semantic image attributes

Reference 32

Resolution
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raw_fallback, observed 2026-08-14T12:51:03.611284Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-14T12:51:03.318520Z digest=sha256:884bd6576d2a2a53998a0067a02ac186520835b3303eb0c75d7fa3fc46108e81

Observation 44767337-0a16-4ae1-a4ae-c00245c197ab · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 33

Resolution
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no resolver link, observed 2026-08-14T12:51:03.320951Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T12:51:03.320951Z digest=sha256:aaedc7f7e8544c899a1d3670e1fc55e581d44d47536f0397e35ac1d8f0ca15af

Observation 5fd96940-9ff0-4a19-84cd-26e493a803f6 · outbound

This paper cites Recovering Realistic Texture in Image Super-resolution by Deep Spatial Feature Transform.

RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution Recovering Realistic Texture in Image Super-resolution by Deep Spatial Feature Transform

Reference 34

Resolution
verified exact
local_arxiv, observed 2026-08-14T12:51:03.479477Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-14T12:51:03.324154Z digest=sha256:81eee4ef721d1a74a0c4946d1e11dd70dc2cec4d70c8b23c46dc3031f3a931a1

Observation d9d3c305-845a-4dac-af1a-822737224486 · outbound

This paper cites Esrgan: Enhanced super-resolution generative adversarial networks.

RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution Esrgan: Enhanced super-resolution generative adversarial networks

Reference 35

Resolution
verified fuzzy
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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation dfa22370-4cb0-40e3-a8f6-99f7240156c6 · outbound

This paper cites Deep relative attributes.

RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution Deep relative attributes

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T12:51:03.595060Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 694b9f6b-512d-49b1-9e2d-f737b6da523c · outbound

This paper cites No-reference image quality assessment based on visual codebook.

RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution No-reference image quality assessment based on visual codebook

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T12:51:03.586342Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation ec02a61f-507e-4fe9-a893-4e32f4b87ebb · outbound

This paper cites Learning to compare image patches via convolutional neural networks.

RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution Learning to compare image patches via convolutional neural networks

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T12:51:03.578204Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 6c64696f-0088-4d12-ba29-28df3f0f0825 · outbound

This paper cites On single image scale-up using sparse-representations.

RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution On single image scale-up using sparse-representations

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T12:51:03.570545Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-14T12:51:03.338669Z digest=sha256:c07e54127558dcbe845b22a075ddd54a9eaf9aaf4046e39618e0a9a60dfcdd4e

Observation db199ba3-0686-41c5-8d4a-4847c8a110c0 · outbound

This paper cites Single image super-resolution with non-local means and steering kernel regression.

RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution Single image super-resolution with non-local means and steering kernel regression

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T12:51:03.561676Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation b804b174-94f6-4078-8e63-c71105f2cb19 · outbound

This paper cites An edge-guided image interpolation algorithm via directional filtering and data fusion.

RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution An edge-guided image interpolation algorithm via directional filtering and data fusion

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T12:51:03.554170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-14T12:51:03.345288Z digest=sha256:7eab89dd521205af987b5b3263fbefa0393b0d09fdb3655a01549b1d4279de32

Observation f08c85ad-d99c-4538-8bd4-f8336e90e7a9 · outbound

This paper cites Residual dense network for image super-resolution.

RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution Residual dense network for image super-resolution

Reference 42

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-14T12:51:03.347809Z digest=sha256:fab6750a2fe824112e085ac920facca1cea0ba4c38d817422d88dd9d2f6f1818

Observation cffc2014-f611-4193-8efe-8ce7b861e186 · outbound

This paper cites write newline.

RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution write newline

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-14T12:51:03.350275Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Pith citing papers

No inbound Pith citation observations are available.