Paper Citation Record · LEDGER

W-Net: A Deep Model for Fully Unsupervised Image Segmentation

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:1711.08506.

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

pith.paper-citation-record.v1
1711.08506 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T10:32:25.229571Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-05-25T11:40:44.492452Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 52bb12af-5840-402e-954c-0af34cfcb1bb · inbound

DeepTEGINN: Deep Learning Based Tools to Extract Graphs from Images of Neural Networks cites this paper.

DeepTEGINN: Deep Learning Based Tools to Extract Graphs from Images of Neural Networks W-Net: A Deep Model for Fully Unsupervised Image Segmentation

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-05-25T11:40:44.494618Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 94609d40-f0be-43bc-b287-604cd4296d7d · inbound

Understanding Deep Learning Techniques for Image Segmentation cites this paper.

Understanding Deep Learning Techniques for Image Segmentation W-Net: A Deep Model for Fully Unsupervised Image Segmentation

Reference 207

Resolution: verified exact; local_arxiv, observed 2026-05-24T21:46:24.334108Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 09ec3b56-397b-438c-9de2-93c61cb405c4 · inbound

Incremental Class Discovery for Semantic Segmentation with RGBD Sensing cites this paper.

Incremental Class Discovery for Semantic Segmentation with RGBD Sensing W-Net: A Deep Model for Fully Unsupervised Image Segmentation

Reference 39

Resolution: verified exact; local_arxiv, observed 2026-05-24T17:24:44.792800Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d3e52552-5bdf-4856-b550-48fa2e780b97 · inbound

Metric-based Regularization and Temporal Ensemble for Multi-task Learning using Heterogeneous Unsupervised Tasks cites this paper.

Metric-based Regularization and Temporal Ensemble for Multi-task Learning using Heterogeneous Unsupervised Tasks W-Net: A Deep Model for Fully Unsupervised Image Segmentation

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-14T10:32:25.229571Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 08e67d93-ef26-44b1-942d-a3d7c8e4cad0 · inbound

Learning Rich Representations For Structured Visual Prediction Tasks cites this paper.

Learning Rich Representations For Structured Visual Prediction Tasks W-Net: A Deep Model for Fully Unsupervised Image Segmentation

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-14T10:12:09.010770Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0dbb0fb1-48b8-4cc1-8997-68c723de8a5e · inbound

Hierarchical-embedding autoencoder with a predictor (HEAP) as efficient architecture for learning long-term evolution of complex multi-scale physical systems cites this paper.

Hierarchical-embedding autoencoder with a predictor (HEAP) as efficient architecture for learning long-term evolution of complex multi-scale physical systems W-Net: A Deep Model for Fully Unsupervised Image Segmentation

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-07T14:27:44.163314Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0361cb6b-b2a3-40a7-80e2-a45732bc3bc1 · inbound

SyncMapV2: Robust and Adaptive Unsupervised Segmentation cites this paper.

SyncMapV2: Robust and Adaptive Unsupervised Segmentation W-Net: A Deep Model for Fully Unsupervised Image Segmentation

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T23:51:02.340446Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3c4de699-4fd3-42d3-855e-5e2671745e65 · inbound

Post Processing of image segmentation using Conditional Random Fields cites this paper.

Post Processing of image segmentation using Conditional Random Fields W-Net: A Deep Model for Fully Unsupervised Image Segmentation

Reference 4

Resolution: metadata mismatch; local_arxiv, observed 2026-05-21T20:20:34.616535Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7fb35975-925a-4780-82c4-3b730c5bf14d · inbound

Segmenting Low-Contrast XCTs of Concrete: An Unsupervised Approach cites this paper.

Segmenting Low-Contrast XCTs of Concrete: An Unsupervised Approach W-Net: A Deep Model for Fully Unsupervised Image Segmentation

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-02T21:35:53.882400Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4c0747e4-e286-4185-bb3a-a4ace218938c · inbound

Deep Wave Network for Modeling Multi-Scale Physical Dynamics cites this paper.

Deep Wave Network for Modeling Multi-Scale Physical Dynamics W-Net: A Deep Model for Fully Unsupervised Image Segmentation

Reference 74

Resolution: verified exact; arxiv_id, observed 2026-05-11T17:31:04.866730Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation cdecb693-9661-4c85-8659-cdfa1b3b37e7 · inbound

Trajectory-Agnostic Asteroid Detection in TESS with Deep Learning cites this paper.

Trajectory-Agnostic Asteroid Detection in TESS with Deep Learning W-Net: A Deep Model for Fully Unsupervised Image Segmentation

Reference 125

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T03:37:11.617334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.