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Paper Citation Record · LEDGER

Distance Map Loss Penalty Term for Semantic Segmentation

As of 18 August 2026, this Paper Citation Record lists 10 of 10 outbound references and 4 inbound Pith citation observations for arXiv:1908.03679.

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

pith.paper-citation-record.v1
1908.03679 v1

Coverage vector

measured 10 of 10 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T14:09:28.682926Z

measured 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T15:28:35.416971Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-10T11:00:03.879779Z

Reference resolution

10 of 10 outbound references displayed

  • verified exact1
  • verified fuzzy4
  • unresolved5
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e85598a3-3a97-462b-9590-6eff798c5fe0 · outbound

This paper cites Tensorflow: A system for large-scale machine learning.

Distance Map Loss Penalty Term for Semantic Segmentation Tensorflow: A system for large-scale machine learning

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T14:09:28.847317Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-14T14:09:28.638966Z digest=sha256:e619105c9d578513d565e65ee1be6bc978b6b3cde77ea0bd2888238bf8dc1a1a

Observation af53923a-0230-4ab6-8903-98769cb4601c · outbound

This paper cites Deep residual learning for image recognition.

Distance Map Loss Penalty Term for Semantic Segmentation Deep residual learning for image recognition

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-14T14:09:28.643752Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T14:09:28.643752Z digest=sha256:6b3c85368b3a125bc0875daf6710b6df5c65d128ec10d16bcf4c29805286cb20

Observation a9396162-efa8-45a8-a815-7121db2f03f4 · outbound

This paper cites Boundary loss for highly unbalanced segmentation.

Distance Map Loss Penalty Term for Semantic Segmentation Boundary loss for highly unbalanced segmentation

Reference 3

Resolution
verified exact
local_arxiv, observed 2026-08-14T14:09:28.756861Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-14T14:09:28.648486Z digest=sha256:a62413c385482ebc96953e610d452011d9d947026a5ba66905933c46c2831c2a

Observation 6eb31b32-1a46-4f2d-a962-c1e966bf73c0 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Distance Map Loss Penalty Term for Semantic Segmentation Adam: A Method for Stochastic Optimization

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-14T14:09:28.653496Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T14:09:28.653496Z digest=sha256:35b41f98b9bb396945f230a2970d948785652938d74ca4230999a598444bb990

Observation c5a47677-ec87-474a-af78-cfead2f7159b · outbound

This paper cites Focal loss for dense object detection.

Distance Map Loss Penalty Term for Semantic Segmentation Focal loss for dense object detection

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-14T14:09:28.659625Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T14:09:28.659625Z digest=sha256:7358709a3d547309c37c71417cbba481680fafcbb7411e82eeff05061d591a3a

Observation 9ec04810-d914-4e8f-9565-91b9fbb07216 · outbound

This paper cites The mathworks.

Distance Map Loss Penalty Term for Semantic Segmentation The mathworks

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T14:09:28.813406Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-14T14:09:28.664298Z digest=sha256:9b19e338d9f9bb6f4fc84abbd1a0986c12b38599a72826cd2d469e328187bb91

Observation fadb6fb8-3106-4faf-9ece-166d7071d832 · outbound

This paper cites V-net: Fully convolutional neural networks for volumetric medical image segmentation.

Distance Map Loss Penalty Term for Semantic Segmentation V-net: Fully convolutional neural networks for volumetric medical image segmentation

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T14:09:28.798591Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-14T14:09:28.669441Z digest=sha256:8ac551f52a4529b8c911690380ca89f2de86f667ab5d404e2db5af1810bf39c7

Observation 5fadd366-cc07-4de6-b632-97d350b667ca · outbound

This paper cites Use of 2d u-net convolutional neural networks for automated cartilage and meniscus segmentation of knee mr imaging data to determine relaxometry and morphometry.

Distance Map Loss Penalty Term for Semantic Segmentation Use of 2d u-net convolutional neural networks for automated cartilage and meniscus segmentation of knee mr imaging data to determine relaxometry and morphometry

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T14:09:28.783551Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-14T14:09:28.673633Z digest=sha256:754affc9679746f45be80b212b6b50e64af99937c1e410eb96d3a2b3df6bf094

Observation 1471ca24-3213-47a6-bb9d-acaa149bda20 · outbound

This paper cites Regularizing Neural Networks by Penalizing Confident Output Distributions.

Distance Map Loss Penalty Term for Semantic Segmentation Regularizing Neural Networks by Penalizing Confident Output Distributions

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-14T14:09:28.677978Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T14:09:28.677978Z digest=sha256:6926b6a90a8f526b157b76b6e4c9fab9117252ce39be3ade255f89b8d4b0fbca

Observation 0d4b08e6-0b0a-4698-bb2d-dca9436824d9 · outbound

This paper cites U-net: Convolutional networks for biomedical image segmentation.

Distance Map Loss Penalty Term for Semantic Segmentation U-net: Convolutional networks for biomedical image segmentation

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-14T14:09:28.682926Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T14:09:28.682926Z digest=sha256:5cf8baa6573abf56e957b2bf6dde294ef7140878fc38210cfea15bc6a85223df

Pith citing papers

Observation e79308cd-b293-4ef1-a847-af99e0e76a4f · inbound

Learning Pore-scale Multi-phase Flow from Experimental Data with Graph Neural Network cites this paper.

Learning Pore-scale Multi-phase Flow from Experimental Data with Graph Neural Network Distance Map Loss Penalty Term for Semantic Segmentation

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-12T15:28:35.416971Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T15:28:35.416971Z digest=sha256:7dc964cbbb2c0ff69602c2225e39bc5fb31f187f399e62ac26283455a3c8310b

Observation 77c8debb-a057-4b23-9e43-0e615b847aa2 · inbound

A Novel Shape Guided Transformer Network for Instance Segmentation in Remote Sensing Images cites this paper.

A Novel Shape Guided Transformer Network for Instance Segmentation in Remote Sensing Images Distance Map Loss Penalty Term for Semantic Segmentation

Reference 63

Resolution
unresolved
no resolver link, observed 2026-08-10T22:56:50.755260Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T22:56:50.755260Z digest=sha256:c2e542b0b4eaca52d07a4737c495aadb5f6d8d2dcf322b4786846030b90674fb

Observation a4d6396d-c62a-45b3-abf8-01013e0cdb23 · inbound

Critical edge sets in vertex-critical graphs cites this paper.

Critical edge sets in vertex-critical graphs Distance Map Loss Penalty Term for Semantic Segmentation

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-05T21:28:12.663955Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T21:28:12.663955Z digest=sha256:d8471c6d145cefa153e4aa29893413585463c9d6d74b035b2a8b38191a6d4752

Observation bc592340-8cdd-4d43-91d8-d2733f4b69e8 · inbound

From Boundaries to Semantics: Prompt-Guided Multi-Task Learning for Petrographic Thin-section Segmentation cites this paper.

From Boundaries to Semantics: Prompt-Guided Multi-Task Learning for Petrographic Thin-section Segmentation Distance Map Loss Penalty Term for Semantic Segmentation

Reference 49

Resolution
verified exact
arxiv_id, observed 2026-05-10T11:00:03.881484Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-10T10:59:28.266817Z digest=sha256:1d7b7ac6707c0221545086cfef9cea39e02ed01fc215eb8ed98812c7c66e8514