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Paper Citation Record · LEDGER

Hybrid Deep Learning for Traceability and Classification of Industrial Slate Tiles

As of 9 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 0 inbound Pith citation observations for arXiv:2607.04811.

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

pith.paper-citation-record.v1
2607.04811 v1

Coverage vector

measured 23 of 23 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-11T13:10:06.234515Z

measured 23 of 23 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 0 of 0 inbound itemization

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measured 0 of 1 external citation measurements

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Reference resolution

23 of 23 outbound references displayed

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Outbound references

Observation a1ff70a0-c501-49f1-bfe2-d8a508e7f2ae · outbound

This paper cites Understanding the Benefits of Natural Slate |Slate Association,.

Hybrid Deep Learning for Traceability and Classification of Industrial Slate Tiles Understanding the Benefits of Natural Slate |Slate Association,

Reference 1

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Observation c953e074-5f72-4d4d-a4a8-534b2c276591 · outbound

This paper cites C406C406M-15 Standard Specification For Roofing Slate|PDF|Roof|Slate.

Hybrid Deep Learning for Traceability and Classification of Industrial Slate Tiles C406C406M-15 Standard Specification For Roofing Slate|PDF|Roof|Slate

Reference 2

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Observation 8c78ac83-7957-4850-89a8-cdcd264ecefb · outbound

This paper cites Tacit knowledge elicitation process for industry 4.0,.

Hybrid Deep Learning for Traceability and Classification of Industrial Slate Tiles Tacit knowledge elicitation process for industry 4.0,

Reference 3

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Observation a3165a07-ff00-4d7e-bc8b-ce63fc13ffc1 · outbound

This paper cites How and why we need to capture tacit knowledge in man- ufacturing: Case studies of visual inspection,.

Hybrid Deep Learning for Traceability and Classification of Industrial Slate Tiles How and why we need to capture tacit knowledge in man- ufacturing: Case studies of visual inspection,

Reference 4

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Observation 1e19c76a-d7e8-45f3-bf20-f5386eebb989 · outbound

This paper cites Slate detection in orthophotos of building roof panels,.

Hybrid Deep Learning for Traceability and Classification of Industrial Slate Tiles Slate detection in orthophotos of building roof panels,

Reference 5

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Observation 25b47b66-7f3b-481a-9457-03e46fba4b74 · outbound

This paper cites Deep learning-based automated tile defect detection system for Portuguese cultural heritage buildings,.

Hybrid Deep Learning for Traceability and Classification of Industrial Slate Tiles Deep learning-based automated tile defect detection system for Portuguese cultural heritage buildings,

Reference 6

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Observation 6bed2061-87cd-4fde-9d4f-616fe4c57be0 · outbound

This paper cites RoMa: Robust Dense Feature Matching,.

Hybrid Deep Learning for Traceability and Classification of Industrial Slate Tiles RoMa: Robust Dense Feature Matching,

Reference 7

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Observation 7d2efe7b-8535-4eea-ac3c-3efb5e84060c · outbound

This paper cites SuperGlue: Learning Feature Matching with Graph Neural Networks,.

Hybrid Deep Learning for Traceability and Classification of Industrial Slate Tiles SuperGlue: Learning Feature Matching with Graph Neural Networks,

Reference 8

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Observation 111613a5-45f3-43a9-a144-0ec4d83140f1 · outbound

This paper cites Deep Residual Learning for Image Recognition,.

Hybrid Deep Learning for Traceability and Classification of Industrial Slate Tiles Deep Residual Learning for Image Recognition,

Reference 9

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Observation d5e6506a-0a81-4c7f-801d-ab3dea6b91ce · outbound

This paper cites XFeat: Accelerated Features for Lightweight Image Matching.

Hybrid Deep Learning for Traceability and Classification of Industrial Slate Tiles XFeat: Accelerated Features for Lightweight Image Matching

Reference 10

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Observation 40303013-f663-404f-9373-f410d47398ea · outbound

This paper cites Searching for MobileNetV3,.

Hybrid Deep Learning for Traceability and Classification of Industrial Slate Tiles Searching for MobileNetV3,

Reference 11

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Observation ac39351f-9d62-4851-aae6-90bb3e5e8cfe · outbound

This paper cites LightGlue: Local Feature Matching at Light Speed.

Hybrid Deep Learning for Traceability and Classification of Industrial Slate Tiles LightGlue: Local Feature Matching at Light Speed

Reference 12

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Observation 20fa81f2-4e02-45cf-8fbf-ce7e00c3c2ae · outbound

This paper cites MegaDepth: Learning Single- View Depth Prediction from Internet Photos,.

Hybrid Deep Learning for Traceability and Classification of Industrial Slate Tiles MegaDepth: Learning Single- View Depth Prediction from Internet Photos,

Reference 13

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Observation fd971d7a-ef08-406b-95bf-fbb179260457 · outbound

This paper cites Gradient-based learning applied to document recogni- tion,.

Hybrid Deep Learning for Traceability and Classification of Industrial Slate Tiles Gradient-based learning applied to document recogni- tion,

Reference 14

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Observation 863e7ac0-7d63-492a-bf18-71da25d10daa · outbound

This paper cites Microsoft COCO: Common Objects in Context,.

Hybrid Deep Learning for Traceability and Classification of Industrial Slate Tiles Microsoft COCO: Common Objects in Context,

Reference 15

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Observation 2989efa6-b029-4779-900d-1384cdb85fe4 · outbound

This paper cites Su- perPoint: Self-Supervised Interest Point Detection and Description,.

Hybrid Deep Learning for Traceability and Classification of Industrial Slate Tiles Su- perPoint: Self-Supervised Interest Point Detection and Description,

Reference 16

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Observation 7269672c-28d6-4ccc-8676-0d6fc207d0af · outbound

This paper cites ImageNet classification with deep convolutional neural networks,.

Hybrid Deep Learning for Traceability and Classification of Industrial Slate Tiles ImageNet classification with deep convolutional neural networks,

Reference 17

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Observation 39f949e2-f46e-44e8-af45-d12d4a17b459 · outbound

This paper cites MobileNetV2: Inverted Residuals and Linear Bottlenecks,.

Hybrid Deep Learning for Traceability and Classification of Industrial Slate Tiles MobileNetV2: Inverted Residuals and Linear Bottlenecks,

Reference 18

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Observation b98eccd8-baa7-4255-a912-4e6486bd9fad · outbound

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

Hybrid Deep Learning for Traceability and Classification of Industrial Slate Tiles MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications,

Reference 19

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Observation 997237af-b329-4b04-8a71-426e28675c11 · outbound

This paper cites MnasNet: Platform-Aware Neural Architecture Search for Mobile,.

Hybrid Deep Learning for Traceability and Classification of Industrial Slate Tiles MnasNet: Platform-Aware Neural Architecture Search for Mobile,

Reference 20

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Observation 1467f534-5a34-49a4-b3b3-1af39c216e50 · outbound

This paper cites GlueStick: Robust Image Matching by Sticking Points and Lines Together,.

Hybrid Deep Learning for Traceability and Classification of Industrial Slate Tiles GlueStick: Robust Image Matching by Sticking Points and Lines Together,

Reference 21

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Observation 856a301d-8045-4f23-9475-791efb28b524 · outbound

This paper cites SAM 2: Segment Any- thing in Images and Videos,.

Hybrid Deep Learning for Traceability and Classification of Industrial Slate Tiles SAM 2: Segment Any- thing in Images and Videos,

Reference 22

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Observation 96826d2a-7716-468b-b2b8-a9568361f65e · outbound

This paper cites Steerers: A framework for rotation equivariant keypoint descriptors,.

Hybrid Deep Learning for Traceability and Classification of Industrial Slate Tiles Steerers: A framework for rotation equivariant keypoint descriptors,

Reference 23

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