Paper Citation Record · LEDGER

CNNtention: Can CNNs do better with Attention?

As of 14 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2412.11657.

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

pith.paper-citation-record.v1
2412.11657 v3

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T14:45:08.369833Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+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

31 of 31 outbound references displayed

verified exact1
verified fuzzy24
unresolved6
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9ad562fa-a3a0-449d-ac8b-9ccb32ec7110 · outbound

This paper cites Deep convolutional neu- ral networks for image classification: A comprehensive re- view.

CNNtention: Can CNNs do better with Attention? Deep convolutional neu- ral networks for image classification: A comprehensive re- view

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:45:08.755688Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 3f761901-e2ab-45b9-938f-2e7072d3793c · outbound

This paper cites A review of deep learn- ing in image recognition.

CNNtention: Can CNNs do better with Attention? A review of deep learn- ing in image recognition

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:45:08.741563Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation f4e521b1-3dcb-4ac9-b9ee-579d0d76d9d7 · outbound

This paper cites Cuevas-Tello, Jose Nunez-Varela, Cesar Puente, and Alejandra G.

CNNtention: Can CNNs do better with Attention? Cuevas-Tello, Jose Nunez-Varela, Cesar Puente, and Alejandra G

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:45:08.728956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 86d364e0-c9f4-41af-b4b1-67d1b71b5331 · outbound

This paper cites Contextual convolutional neural net- works, 2021.

CNNtention: Can CNNs do better with Attention? Contextual convolutional neural net- works, 2021

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:45:08.715888Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 4844b80e-ec91-46e7-aacc-45f4dd7bdd1e · outbound

This paper cites Noisynn: Exploring the impact of information entropy change in learning systems, 2024.

CNNtention: Can CNNs do better with Attention? Noisynn: Exploring the impact of information entropy change in learning systems, 2024

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:45:08.702744Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 11127c8a-f88c-4639-8daa-3316ab4ad70e · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale, 2021.

CNNtention: Can CNNs do better with Attention? An image is worth 16x16 words: Transformers for image recognition at scale, 2021

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-11T14:45:08.280218Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 46115417-18ad-4eb0-aad4-70e22214f80c · outbound

This paper cites Do vision trans- formers see like convolutional neural networks?, 2022.

CNNtention: Can CNNs do better with Attention? Do vision trans- formers see like convolutional neural networks?, 2022

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:45:08.681791Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 47aa7432-0445-4cc4-b3e1-4d4e9fa761dd · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

CNNtention: Can CNNs do better with Attention? An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-11T14:45:08.287213Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ace7355e-7467-429e-97d6-ae4b278f9ee4 · outbound

This paper cites Squeeze-and-excitation networks, 2019.

CNNtention: Can CNNs do better with Attention? Squeeze-and-excitation networks, 2019

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:45:08.670394Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 291b66bc-2df0-4ae2-8336-ea45af4b4929 · outbound

This paper cites Spatial transformer networks, 2016.

CNNtention: Can CNNs do better with Attention? Spatial transformer networks, 2016

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:45:08.660747Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 6e18cf2b-8d49-4367-93df-b6e36aa3e6d3 · outbound

This paper cites Attention u-net: Learning where to look for the pancreas, 2018.

CNNtention: Can CNNs do better with Attention? Attention u-net: Learning where to look for the pancreas, 2018

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:45:08.649357Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 8a80f2db-db99-4048-81d7-7186b0f2039f · outbound

This paper cites Csanet: Channel spatial attention network for robust 3d face alignment and reconstruction, 2024.

CNNtention: Can CNNs do better with Attention? Csanet: Channel spatial attention network for robust 3d face alignment and reconstruction, 2024

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:45:08.637316Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b1bc437a-66f1-440a-b4fa-074bd2ddcb1a · outbound

This paper cites Ca-net: Comprehensive attention con- volutional neural networks for explainable medical image segmentation.

CNNtention: Can CNNs do better with Attention? Ca-net: Comprehensive attention con- volutional neural networks for explainable medical image segmentation

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:45:08.626681Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation ec1b4b1c-a594-41da-89b8-3d93935571e7 · outbound

This paper cites Ela: Efficient local attention for deep convolutional neural networks, 2024.

CNNtention: Can CNNs do better with Attention? Ela: Efficient local attention for deep convolutional neural networks, 2024

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:45:08.616851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 8c408fcb-8b5d-4ba9-9b45-aff744fe3d0e · outbound

This paper cites Cbam: Convolutional block attention module, 2018.

CNNtention: Can CNNs do better with Attention? Cbam: Convolutional block attention module, 2018

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:45:08.606207Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c629cf88-fe3a-4889-afa6-29d5b0f301c9 · outbound

This paper cites Cifar- 10 and cifar-100 (canadian institute for advance research).

CNNtention: Can CNNs do better with Attention? Cifar- 10 and cifar-100 (canadian institute for advance research)

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:45:08.595402Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation ee1a602a-9da9-4b5c-bd06-f6b4eb208edc · outbound

This paper cites an unresolved cited work.

CNNtention: Can CNNs do better with Attention? Unresolved cited work

Reference 17

Resolution: unresolved; raw_fallback, observed 2026-08-11T14:45:08.583920Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b031e479-5d0d-445d-9671-92a3f6a0a8db · outbound

This paper cites Learning multiple layers of features from tiny images.

CNNtention: Can CNNs do better with Attention? Learning multiple layers of features from tiny images

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:45:08.571886Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 0e1bc6af-6b9c-46f0-941d-1e7285e971d0 · outbound

This paper cites Deep residual learning for image recognition, 2015.

CNNtention: Can CNNs do better with Attention? Deep residual learning for image recognition, 2015

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:45:08.558935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation e0e3e0b8-0aef-4ec6-bde0-b74c9d2cf1cd · outbound

This paper cites Deep residual learning for image recognition: Cifar-10, pytorch implementation.

CNNtention: Can CNNs do better with Attention? Deep residual learning for image recognition: Cifar-10, pytorch implementation

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:45:08.546170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation bfabe47f-ad4e-4cc3-bd7e-86f148f4f11f · outbound

This paper cites MNIST handwritten digit database.

CNNtention: Can CNNs do better with Attention? MNIST handwritten digit database

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:45:08.532882Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c11f311b-734c-495a-83a3-354f41abd050 · outbound

This paper cites M ¨uller and Karla Markert.

CNNtention: Can CNNs do better with Attention? M ¨uller and Karla Markert

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:45:08.521392Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation e7e1fc15-4ad5-4539-a2a1-4dbbc877de66 · outbound

This paper cites Proper ResNet implementation for CI- FAR10/CIFAR100 in PyTorch.https://github.com/ akamaster/pytorch_resnet_cifar10.

CNNtention: Can CNNs do better with Attention? Proper ResNet implementation for CI- FAR10/CIFAR100 in PyTorch.https://github.com/ akamaster/pytorch_resnet_cifar10

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:45:08.509169Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 134ab9d5-abdc-4913-a709-d5594ca44697 · outbound

This paper cites Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization.

CNNtention: Can CNNs do better with Attention? Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-11T14:45:08.341765Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3a029942-f877-42a5-8a19-7b4a803bf0a7 · outbound

This paper cites CNNten- tion Github Repository.

CNNtention: Can CNNs do better with Attention? CNNten- tion Github Repository

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:45:08.498110Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 67a93661-1f62-46d3-a66a-eabd5f0460f9 · outbound

This paper cites Attention Is All You Need.

CNNtention: Can CNNs do better with Attention? Attention Is All You Need

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-11T14:45:08.349638Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e093fb78-40ca-4403-8f97-e2944d087a3b · outbound

This paper cites an unresolved cited work.

CNNtention: Can CNNs do better with Attention? Unresolved cited work

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-11T14:45:08.353535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2aa709c6-4b51-4054-b6f5-5d46b17517e3 · outbound

This paper cites Self-attention generative adversarial networks,.

CNNtention: Can CNNs do better with Attention? Self-attention generative adversarial networks,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:45:08.480921Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation e131d119-c917-4ce9-889e-074564ea35a2 · outbound

This paper cites Mlflow: An open source platform for the ma- chine learning lifecycle.

CNNtention: Can CNNs do better with Attention? Mlflow: An open source platform for the ma- chine learning lifecycle

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:45:08.469060Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 84a8585d-79c4-4f68-a07c-073b29c858a9 · outbound

This paper cites Weighted Residuals for Very Deep Networks.

CNNtention: Can CNNs do better with Attention? Weighted Residuals for Very Deep Networks

Reference 30

Resolution: verified exact; local_arxiv, observed 2026-08-11T14:45:08.409815Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation a4172184-fbd4-4101-82c2-ec0f501e9d75 · outbound

This paper cites Going deeper with convolutions, 2014.

CNNtention: Can CNNs do better with Attention? Going deeper with convolutions, 2014

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:45:08.457306Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Pith citing papers

No inbound Pith citation observations are available.