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Paper Citation Record · LEDGER

OneNet: A Channel-Wise 1D Convolutional U-Net

As of 13 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2411.09838.

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

pith.paper-citation-record.v1
2411.09838 v1

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T20:20:16.248188Z

measured 30 of 30 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

30 of 30 outbound references displayed

  • verified exact6
  • verified fuzzy11
  • unresolved13
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 57278608-443e-4131-ad28-ead5006bde15 · outbound

This paper cites Land- man, Geert Litjens, Bjoern Menze, Olaf Ronneberger, Ronald M.

OneNet: A Channel-Wise 1D Convolutional U-Net Land- man, Geert Litjens, Bjoern Menze, Olaf Ronneberger, Ronald M

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T20:20:16.105756Z digest=sha256:d9a5b6948e9ca2157ea311121c5c13f425983056e39b146c3c45b551ce1b24eb

Observation c95ca6ef-c98f-4ccc-9c6d-bf3d3163853d · outbound

This paper cites Blitzmask: Real-time instance segmentation approach for mobile devices.

OneNet: A Channel-Wise 1D Convolutional U-Net Blitzmask: Real-time instance segmentation approach for mobile devices

Reference 2

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raw_fallback, observed 2026-08-12T20:20:16.811113Z

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.

source=pdf_text observed=2026-08-12T20:20:16.112270Z digest=sha256:3376da8944113bad4031e9d2658898f3df716342797b677ae350b31078cebdc1

Observation 5fa88490-f533-4e8b-a496-1eb1908bd37a · outbound

This paper cites MultiDepth: Multi-Sample Priors for Refining Monocular Metric Depth Estimations in Indoor Scenes.

OneNet: A Channel-Wise 1D Convolutional U-Net MultiDepth: Multi-Sample Priors for Refining Monocular Metric Depth Estimations in Indoor Scenes

Reference 3

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verified exact
local_arxiv, observed 2026-08-12T20:20:16.533680Z

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.

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Observation 81f0afe4-38d3-4970-9d4f-dda97f8dddb7 · outbound

This paper cites N ¨urnberger.

OneNet: A Channel-Wise 1D Convolutional U-Net N ¨urnberger

Reference 4

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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.

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Observation 9a6c07cd-25c7-4ccb-a017-58790984371c · outbound

This paper cites an unresolved cited work.

OneNet: A Channel-Wise 1D Convolutional U-Net Unresolved cited work

Reference 5

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

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Observation 4720f521-21e4-445a-b16f-67fd33647733 · outbound

This paper cites Deep neural networks segment neu- ronal membranes in electron microscopy images.

OneNet: A Channel-Wise 1D Convolutional U-Net Deep neural networks segment neu- ronal membranes in electron microscopy images

Reference 6

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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.

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Observation eced5e82-a601-462b-981c-e6f93d3adfda · outbound

This paper cites an unresolved cited work.

OneNet: A Channel-Wise 1D Convolutional U-Net Unresolved cited work

Reference 7

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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.

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Observation 291d4b66-e1c3-45f3-99b7-8525fcc2f8dc · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

OneNet: A Channel-Wise 1D Convolutional U-Net Imagenet: A large-scale hierarchical image database

Reference 8

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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.

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Observation af4af67f-0925-4751-b083-06fede7f6d92 · outbound

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

OneNet: A Channel-Wise 1D Convolutional U-Net The pascal visual object classes (voc) challenge

Reference 9

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raw_fallback, observed 2026-08-12T20:20:16.717214Z

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.

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Observation 88737ac1-ab4c-496f-bfb9-09415953f211 · outbound

This paper cites Brain Tumor Segmentation from MRI Images using Deep Learning Techniques.

OneNet: A Channel-Wise 1D Convolutional U-Net Brain Tumor Segmentation from MRI Images using Deep Learning Techniques

Reference 10

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verified exact
local_arxiv, observed 2026-08-12T20:20:16.512559Z

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.

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Observation 30d3bc21-d743-48c0-bea2-188d1aecedec · outbound

This paper cites Zhang, Shaoqing Ren, and Jian Sun.

OneNet: A Channel-Wise 1D Convolutional U-Net Zhang, Shaoqing Ren, and Jian Sun

Reference 11

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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.

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Observation 3a1c59dd-130f-479a-a438-5e06a1988d44 · outbound

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

OneNet: A Channel-Wise 1D Convolutional U-Net MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 12

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no resolver link, observed 2026-08-12T20:20:16.158022Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 570561b8-6ca2-4a6f-bbe3-23555aa616a6 · outbound

This paper cites Adam: A method for stochastic optimization.

OneNet: A Channel-Wise 1D Convolutional U-Net Adam: A method for stochastic optimization

Reference 13

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raw_fallback, observed 2026-08-12T20:20:16.684038Z

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.

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Observation 634c22fb-56b8-4f15-a06a-e08d57bef943 · outbound

This paper cites Convolutional Networks with Oriented 1D Kernels.

OneNet: A Channel-Wise 1D Convolutional U-Net Convolutional Networks with Oriented 1D Kernels

Reference 14

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verified exact
local_arxiv, observed 2026-08-12T20:20:16.469203Z

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.

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Observation bf59dc67-263b-4e14-8b19-a484e528975a · outbound

This paper cites A 1d convolutional network for leaf and time series classification.

OneNet: A Channel-Wise 1D Convolutional U-Net A 1d convolutional network for leaf and time series classification

Reference 15

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verified exact
local_arxiv, observed 2026-08-12T20:20:16.450864Z

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.

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Observation e6140c6d-04fc-4d35-99e5-ff64c7d02f5a · outbound

This paper cites TFNet: Exploiting Temporal Cues for Fast and Accurate LiDAR Semantic Segmentation.

OneNet: A Channel-Wise 1D Convolutional U-Net TFNet: Exploiting Temporal Cues for Fast and Accurate LiDAR Semantic Segmentation

Reference 16

Resolution
verified exact
local_arxiv, observed 2026-08-12T20:20:16.431007Z

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.

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Observation 8e7a082b-2b04-4eb2-bfc6-9232917c9465 · outbound

This paper cites MobileViG: Graph-Based Sparse Attention for Mobile Vision Applications.

OneNet: A Channel-Wise 1D Convolutional U-Net MobileViG: Graph-Based Sparse Attention for Mobile Vision Applications

Reference 17

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T20:20:16.182267Z digest=sha256:aac0afb876383c93fdf366be73c27c41c3bd0cc1e9cfaa237b74ae5c4db91f79

Observation 6a888f81-b77c-439f-a322-3b01d2948c01 · outbound

This paper cites Eunnet: Efficient un-normalized convolution layer for stable training of deep residual networks without batch normaliza- tion layer.

OneNet: A Channel-Wise 1D Convolutional U-Net Eunnet: Efficient un-normalized convolution layer for stable training of deep residual networks without batch normaliza- tion layer

Reference 18

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raw_fallback, observed 2026-08-12T20:20:16.666796Z

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.

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Observation 69791e91-b7cf-46c4-95b1-7ae6d4fc90ef · outbound

This paper cites Attention U-Net: Learning Where to Look for the Pancreas.

OneNet: A Channel-Wise 1D Convolutional U-Net Attention U-Net: Learning Where to Look for the Pancreas

Reference 19

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Observation d65a7c9b-ec42-471b-979c-33578ca824de · outbound

This paper cites Parkhi, Andrea Vedaldi, Andrew Zisserman, and C.

OneNet: A Channel-Wise 1D Convolutional U-Net Parkhi, Andrea Vedaldi, Andrew Zisserman, and C

Reference 20

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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.

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Observation 040cfb98-4ac2-41af-ae17-28bfa782c519 · outbound

This paper cites Automatic differentiation in pytorch.

OneNet: A Channel-Wise 1D Convolutional U-Net Automatic differentiation in pytorch

Reference 21

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source=pdf_text observed=2026-08-12T20:20:16.201900Z digest=sha256:a061eb54b065104ea2c33f35e395975454c693ceadd8d737efe5fccae774f7ae

Observation 9f5baaf1-4e8b-484c-9786-5484f8d910ac · outbound

This paper cites SAM 2: Segment Anything in Images and Videos.

OneNet: A Channel-Wise 1D Convolutional U-Net SAM 2: Segment Anything in Images and Videos

Reference 22

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Unavailable: canonical work link unavailable.

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Observation 39a6dac3-7745-4b2d-80d4-91ae0234ab5d · outbound

This paper cites U-Net: Convolutional Networks for Biomedical Image Segmentation.

OneNet: A Channel-Wise 1D Convolutional U-Net U-Net: Convolutional Networks for Biomedical Image Segmentation

Reference 23

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source=pdf_text observed=2026-08-12T20:20:16.211042Z digest=sha256:fdd724a660b13a667c78ef229e94210ee81080fbb1d0109ed28f7988a9d6934c

Observation e1839fb7-04bc-44d8-b1ab-9db189f95895 · outbound

This paper cites Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network.

OneNet: A Channel-Wise 1D Convolutional U-Net Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network

Reference 24

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source=pdf_text observed=2026-08-12T20:20:16.215157Z digest=sha256:64488bb9cf0d226eb63da889373fbae54dd85fbbfb12bf4d55c517243225c75d

Observation 32845b56-71df-42da-9ddf-84c0dd46017e · outbound

This paper cites FreeU: Free Lunch in Diffusion U-Net.

OneNet: A Channel-Wise 1D Convolutional U-Net FreeU: Free Lunch in Diffusion U-Net

Reference 25

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no resolver link, observed 2026-08-12T20:20:16.219391Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T20:20:16.219391Z digest=sha256:7d5b04d94d0554c4e233f742a5fe8ba4dd93c11dc795a207631bf011ff3b9956

Observation 690df2f4-35be-408e-a066-34a06deb36a2 · outbound

This paper cites PP-MobileSeg: Explore the Fast and Accurate Semantic Segmentation Model on Mobile Devices.

OneNet: A Channel-Wise 1D Convolutional U-Net PP-MobileSeg: Explore the Fast and Accurate Semantic Segmentation Model on Mobile Devices

Reference 26

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verified exact
local_arxiv, observed 2026-08-12T20:20:16.321827Z

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.

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Observation acb70e90-cd19-4497-976c-629726236465 · outbound

This paper cites One-dimensional deep low-rank and sparse network for accelerated mri.

OneNet: A Channel-Wise 1D Convolutional U-Net One-dimensional deep low-rank and sparse network for accelerated mri

Reference 27

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raw_fallback, observed 2026-08-12T20:20:16.630424Z

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.

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Observation a9379500-fa54-4ebd-8924-9c1759fc2b5c · outbound

This paper cites HarmonyView: Harmonizing Consistency and Diversity in One-Image-to-3D.

OneNet: A Channel-Wise 1D Convolutional U-Net HarmonyView: Harmonizing Consistency and Diversity in One-Image-to-3D

Reference 28

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T20:20:16.234359Z digest=sha256:dfcd0717872a705288065ba35058ae7a327d9667ead3d8be62d2e8ff9f813c0b

Observation abc0ce84-af57-49ef-8352-eeefc5bb4ee7 · outbound

This paper cites UNet++: A Nested U-Net Architecture for Medical Image Segmentation.

OneNet: A Channel-Wise 1D Convolutional U-Net UNet++: A Nested U-Net Architecture for Medical Image Segmentation

Reference 29

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T20:20:16.242582Z digest=sha256:eee599d200b27d2d0b23b1a6eb59e88e591f44ae0bfd7dc25ea1bed42f1a0bb4

Observation f98a6ac0-6374-4fce-924d-c476b65dbba8 · outbound

This paper cites In- fer from what you have seen before: Temporally-dependent classifier for semi-supervised video segmentation.

OneNet: A Channel-Wise 1D Convolutional U-Net In- fer from what you have seen before: Temporally-dependent classifier for semi-supervised video segmentation

Reference 30

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verified fuzzy
raw_fallback, observed 2026-08-12T20:20:16.617493Z

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.

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Pith citing papers

No inbound Pith citation observations are available.