Paper Citation Record · LEDGER

Exploring Active Learning for Semiconductor Defect Segmentation

As of 15 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 1 inbound Pith citation observation for arXiv:2507.17359.

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

pith.paper-citation-record.v1
2507.17359 v1

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T14:55:01.058304Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T14:55:00.749416Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T14:55:01.258529Z

Reference resolution

27 of 27 outbound references displayed

verified exact1
verified fuzzy20
unresolved6
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e717dede-eaab-43f4-adf0-14ad283657f8 · outbound

This paper cites Exploring Active Learning for Semiconductor Defect Segmentation.

Exploring Active Learning for Semiconductor Defect Segmentation Exploring Active Learning for Semiconductor Defect Segmentation

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-08-06T14:55:01.269467Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f17f3e30-f5bb-45a6-a061-834042a6ddd7 · outbound

This paper cites Uncertainty- based methods select samples that the current model is most uncertain about to label.

Exploring Active Learning for Semiconductor Defect Segmentation Uncertainty- based methods select samples that the current model is most uncertain about to label

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:55:02.079405Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2a6680a4-954d-4b1a-a03b-927195a0da63 · outbound

This paper cites rareness.

Exploring Active Learning for Semiconductor Defect Segmentation rareness

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:55:02.040869Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 60c19beb-2d1a-4ecf-b141-8e8d7cf23330 · outbound

This paper cites Experimental Setup Datasets Our dataset is compiled from 3D XRM scans of high bandwidth memory structures composed of logic and memory dies.

Exploring Active Learning for Semiconductor Defect Segmentation Experimental Setup Datasets Our dataset is compiled from 3D XRM scans of high bandwidth memory structures composed of logic and memory dies

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:55:02.012042Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ae8de686-bddc-4c05-b0a4-71e8b1479aaa · outbound

This paper cites an unresolved cited work.

Exploring Active Learning for Semiconductor Defect Segmentation Unresolved cited work

Reference 5

Resolution: unresolved; raw_fallback, observed 2026-08-06T14:55:01.933684Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a6f6edeb-4d05-44ba-9b25-0fedae6f1fc2 · outbound

This paper cites Active Learning for Convolutional Neural Networks: A Core-Set Approach.

Exploring Active Learning for Semiconductor Defect Segmentation Active Learning for Convolutional Neural Networks: A Core-Set Approach

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T14:55:00.889730Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6debdca3-8e2d-4ac3-8f72-dac514db1bc6 · outbound

This paper cites Deep Learning Analysis of 3D X-ray Images for Automated Object Detection and Attribute Measure- ment of Buried Package Features,.

Exploring Active Learning for Semiconductor Defect Segmentation Deep Learning Analysis of 3D X-ray Images for Automated Object Detection and Attribute Measure- ment of Buried Package Features,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:55:01.907255Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 8f0ddd8f-6c96-49ec-83d4-15a1c7e014f8 · outbound

This paper cites Au- tomated attribute measurements of buried package fea- tures in 3D X-ray images using deep learning,.

Exploring Active Learning for Semiconductor Defect Segmentation Au- tomated attribute measurements of buried package fea- tures in 3D X-ray images using deep learning,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:55:01.874934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 239033a1-5149-4bda-9112-01d9e6099220 · outbound

This paper cites Automated Detection and Segmentation of HBMs in 3D X-ray Images using Semi-Supervised Deep Learning,.

Exploring Active Learning for Semiconductor Defect Segmentation Automated Detection and Segmentation of HBMs in 3D X-ray Images using Semi-Supervised Deep Learning,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:55:01.842988Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 17ce3ebc-0aa4-4a48-beb0-8e3d4ac25c41 · outbound

This paper cites The power of ensembles for active learning in image classification,.

Exploring Active Learning for Semiconductor Defect Segmentation The power of ensembles for active learning in image classification,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:55:01.815303Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ec6ec584-85f4-478a-967d-9f2e5efa2725 · outbound

This paper cites Learning loss for active learning,.

Exploring Active Learning for Semiconductor Defect Segmentation Learning loss for active learning,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:55:01.767479Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b7df0c81-4493-48a1-979b-a166db4f9b91 · outbound

This paper cites Exploring simple siamese representation learning,.

Exploring Active Learning for Semiconductor Defect Segmentation Exploring simple siamese representation learning,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:55:01.577472Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 007f90eb-43aa-4808-aa11-d3df7946bcf7 · outbound

This paper cites Sequential graph convolutional network for ac- tive learning,.

Exploring Active Learning for Semiconductor Defect Segmentation Sequential graph convolutional network for ac- tive learning,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:55:01.733222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 9570433b-7a38-45f9-8920-2dc69ab62ff3 · outbound

This paper cites Variational adversarial active learning,.

Exploring Active Learning for Semiconductor Defect Segmentation Variational adversarial active learning,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:55:01.691242Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2d1ed58d-c871-49c6-8b82-f885e01aad48 · outbound

This paper cites Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds.

Exploring Active Learning for Semiconductor Defect Segmentation Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T14:55:00.917194Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7b6688b9-0465-4952-a6f2-fc81d1c7904d · outbound

This paper cites A simple framework for contrastive learning of visual representations,.

Exploring Active Learning for Semiconductor Defect Segmentation A simple framework for contrastive learning of visual representations,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:55:01.655351Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 7e84f21d-8219-4672-b7b7-f3757fe9497f · outbound

This paper cites Momentum contrast for unsupervised visual representation learning,.

Exploring Active Learning for Semiconductor Defect Segmentation Momentum contrast for unsupervised visual representation learning,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:55:01.608446Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 02cfeb96-f511-40c9-b69f-9189664099d8 · outbound

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

Exploring Active Learning for Semiconductor Defect Segmentation U-net: Convolutional networks for biomedical image segmentation,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T14:55:01.016877Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9eb97e6a-a721-40af-86be-791b81ce6ef4 · outbound

This paper cites During each AL cycle, the model is trained with RMSprop optimizer with weighted cross entropy loss.

Exploring Active Learning for Semiconductor Defect Segmentation During each AL cycle, the model is trained with RMSprop optimizer with weighted cross entropy loss

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:55:01.973606Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 26293f1b-e99e-4bee-9bd6-9c331282abf6 · outbound

This paper cites Rethinking deep active learning: Using unlabeled data at model training,.

Exploring Active Learning for Semiconductor Defect Segmentation Rethinking deep active learning: Using unlabeled data at model training,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:55:01.545692Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 961bb29a-b060-4db2-991d-c5c610329ea2 · outbound

This paper cites Deep active learning for biased datasets via fisher kernel self-supervision,.

Exploring Active Learning for Semiconductor Defect Segmentation Deep active learning for biased datasets via fisher kernel self-supervision,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:55:01.515390Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation eed52d0a-f864-4c01-89d4-37d9dc13cf19 · outbound

This paper cites Representation learning with contrastive predictive coding,.

Exploring Active Learning for Semiconductor Defect Segmentation Representation learning with contrastive predictive coding,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:55:01.492347Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a2952027-0251-4264-bdd8-c5a8ff31a941 · outbound

This paper cites Improved Baselines with Momentum Contrastive Learning.

Exploring Active Learning for Semiconductor Defect Segmentation Improved Baselines with Momentum Contrastive Learning

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-06T14:55:01.002528Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b4e8208e-e799-424b-848f-57c7c123c029 · outbound

This paper cites Revisiting superpixels for active learning in semantic segmentation with realistic annotation costs,.

Exploring Active Learning for Semiconductor Defect Segmentation Revisiting superpixels for active learning in semantic segmentation with realistic annotation costs,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:55:01.460374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c098d4b2-232a-48ee-bf87-e9536a8ab7df · outbound

This paper cites Deep residual learning for image recognition,.

Exploring Active Learning for Semiconductor Defect Segmentation Deep residual learning for image recognition,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-06T14:55:01.035501Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9dcc80f2-e6f7-4e2a-a764-785a5ab632fb · outbound

This paper cites Segmentation models py- torch,.

Exploring Active Learning for Semiconductor Defect Segmentation Segmentation models py- torch,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:55:01.341969Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 73ee60ba-9e43-4c7f-9aee-afc67e8b6f12 · outbound

This paper cites Self-damaging contrastive learn- ing,.

Exploring Active Learning for Semiconductor Defect Segmentation Self-damaging contrastive learn- ing,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:55:01.302675Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Pith citing papers

Observation e717dede-eaab-43f4-adf0-14ad283657f8 · inbound