Paper Citation Record · LEDGER

Attention-Aware Linear Depthwise Convolution for Single Image Super-Resolution

As of 16 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 0 inbound Pith citation observations for arXiv:1908.02648.

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

pith.paper-citation-record.v1
1908.02648 v3

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T14:43:08.547722Z

measured 24 of 24 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+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

24 of 24 outbound references displayed

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verified fuzzy11
unresolved13
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c8e0dc3f-2b2c-400d-a0cf-8277d68316bb · outbound

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

Attention-Aware Linear Depthwise Convolution for Single Image Super-Resolution An edge-guided image interpolation algorithm via directional ﬁltering and data fusion,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:43:08.943810Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 2f49a514-733c-447b-9378-174aed2550a1 · outbound

This paper cites Learning a deep convolu- tional network for image super-resolution,.

Attention-Aware Linear Depthwise Convolution for Single Image Super-Resolution Learning a deep convolu- tional network for image super-resolution,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:43:08.929257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 275fcf66-6e08-4e7c-a7ac-8212cd034222 · outbound

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

Attention-Aware Linear Depthwise Convolution for Single Image Super-Resolution Accurate image super-resolution using very deep convolutional networks,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:43:08.915019Z

Source-reported events for the cited work

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Observation 08c17c1c-1030-4758-a6fa-cdea8e6e18fb · outbound

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

Attention-Aware Linear Depthwise Convolution for Single Image Super-Resolution Enhanced deep residual networks for single image super-resolution,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-14T14:43:08.450129Z

Source-reported events for the cited work

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Observation 191961ce-958d-4598-9065-1d836b2d53fd · outbound

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

Attention-Aware Linear Depthwise Convolution for Single Image Super-Resolution Residual dense network for image super-resolution,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-14T14:43:08.455709Z

Source-reported events for the cited work

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Observation c2b1f280-2984-443e-9d73-f5ed1b5e0012 · outbound

This paper cites MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.

Attention-Aware Linear Depthwise Convolution for Single Image Super-Resolution MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-14T14:43:08.460556Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e588fcf1-7ce2-4b2d-9c34-8d1072afa378 · outbound

This paper cites Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift.

Attention-Aware Linear Depthwise Convolution for Single Image Super-Resolution Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-14T14:43:08.465864Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c6a1b3b1-c53c-45ab-9cb9-94eeecc6091f · outbound

This paper cites Squeeze-and-excitation networks,.

Attention-Aware Linear Depthwise Convolution for Single Image Super-Resolution Squeeze-and-excitation networks,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-14T14:43:08.470549Z

Source-reported events for the cited work

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Observation 149f4b8a-108f-4800-bfad-bd9ca3360c00 · outbound

This paper cites Image super- resolution using very deep residual channel attention networks,.

Attention-Aware Linear Depthwise Convolution for Single Image Super-Resolution Image super- resolution using very deep residual channel attention networks,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-14T14:43:08.474861Z

Source-reported events for the cited work

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Observation af77b846-3f0f-44ee-91e1-9f077086f331 · outbound

This paper cites Second-order attention network for single image super-resolution,.

Attention-Aware Linear Depthwise Convolution for Single Image Super-Resolution Second-order attention network for single image super-resolution,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:43:08.851001Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a1c92a2b-960f-43a5-8c07-baedf4bb8743 · outbound

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

Attention-Aware Linear Depthwise Convolution for Single Image Super-Resolution Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-14T14:43:08.483646Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b400545f-bd9a-4ba5-bd2b-f5faa9bfe148 · outbound

This paper cites Residual attention network for image classiﬁcation,.

Attention-Aware Linear Depthwise Convolution for Single Image Super-Resolution Residual attention network for image classiﬁcation,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-14T14:43:08.489090Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6f9edd84-2d28-4211-9661-e01c78061541 · outbound

This paper cites Deep residual learning for image recognition,.

Attention-Aware Linear Depthwise Convolution for Single Image Super-Resolution Deep residual learning for image recognition,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-14T14:43:08.495910Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d458d2f1-a62e-44ed-bad0-623f64fa506a · outbound

This paper cites Real-time single image and video super- resolution using an efﬁcient sub-pixel convolutional neural network,.

Attention-Aware Linear Depthwise Convolution for Single Image Super-Resolution Real-time single image and video super- resolution using an efﬁcient sub-pixel convolutional neural network,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:43:08.795718Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 1625fd51-0893-434c-8840-c765ca700522 · outbound

This paper cites Deep laplacian pyramid networks for fast and accurate super-resolution,.

Attention-Aware Linear Depthwise Convolution for Single Image Super-Resolution Deep laplacian pyramid networks for fast and accurate super-resolution,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:43:08.767680Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 8496caa7-b2ab-4ee7-8956-9c814383b6b3 · outbound

This paper cites Memnet: A persistent memory network for image restoration,.

Attention-Aware Linear Depthwise Convolution for Single Image Super-Resolution Memnet: A persistent memory network for image restoration,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:43:08.746418Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 85090bb4-e55a-4db0-a328-18743f11f9f9 · outbound

This paper cites Fast and accurate single image super- resolution via information distillation network,.

Attention-Aware Linear Depthwise Convolution for Single Image Super-Resolution Fast and accurate single image super- resolution via information distillation network,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-14T14:43:08.514199Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 477e986a-0b1b-4248-a8c0-724fff6e6b9a · outbound

This paper cites Ntire 2017 challenge on single image super-resolution: Methods and results,.

Attention-Aware Linear Depthwise Convolution for Single Image Super-Resolution Ntire 2017 challenge on single image super-resolution: Methods and results,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:43:08.718853Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation cb985c02-f59f-4a13-8648-e5bed6ad5637 · outbound

This paper cites Low- complexity single-image super-resolution based on nonnegative neighbor embedding,.

Attention-Aware Linear Depthwise Convolution for Single Image Super-Resolution Low- complexity single-image super-resolution based on nonnegative neighbor embedding,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-14T14:43:08.522288Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 815c28d9-1758-491b-ac5c-23964152800e · outbound

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

Attention-Aware Linear Depthwise Convolution for Single Image Super-Resolution On single image scale-up using sparse-representations,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-14T14:43:08.526821Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 80c74ca8-86f7-43d7-b5ac-a0fced487989 · outbound

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

Attention-Aware Linear Depthwise Convolution for Single Image Super-Resolution A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:43:08.683369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ced4c782-58fc-48d7-a5f4-7d4fb36d1723 · outbound

This paper cites Single image super-resolution from transformed self-exemplars,.

Attention-Aware Linear Depthwise Convolution for Single Image Super-Resolution Single image super-resolution from transformed self-exemplars,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:43:08.668656Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 64a736e2-ec6b-4ffa-b175-8f7d4666f018 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Attention-Aware Linear Depthwise Convolution for Single Image Super-Resolution Adam: A Method for Stochastic Optimization

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-14T14:43:08.542830Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation daa383f4-4708-4bf4-ba05-a5960e3ed1ab · outbound

This paper cites Automatic differentiation in pytorch,.

Attention-Aware Linear Depthwise Convolution for Single Image Super-Resolution Automatic differentiation in pytorch,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:43:08.649834Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Pith citing papers

No inbound Pith citation observations are available.