Paper Citation Record · LEDGER

Toward quantitative fractography using convolutional neural networks

As of 18 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:1908.02242.

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

pith.paper-citation-record.v1
1908.02242 v2

Coverage vector

measured 44 of 44 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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Reference resolution

44 of 44 outbound references displayed

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External citation measurements

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Outbound references

Observation 64471d5f-411c-4bf1-97af-6a985ef62034 · outbound

This paper cites R., Destefani, J.

Toward quantitative fractography using convolutional neural networks R., Destefani, J

Reference 1

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Observation 02568cb4-8a7b-45db-bb07-7d642078419a · outbound

This paper cites The fracture of metals.

Toward quantitative fractography using convolutional neural networks The fracture of metals

Reference 2

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Toward quantitative fractography using convolutional neural networks Unresolved cited work

Reference 3

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This paper cites L., Busso, E.

Toward quantitative fractography using convolutional neural networks L., Busso, E

Reference 4

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Toward quantitative fractography using convolutional neural networks Unresolved cited work

Reference 5

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This paper cites & McCauley, J.

Toward quantitative fractography using convolutional neural networks & McCauley, J

Reference 6

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This paper cites & Rittel, D.

Toward quantitative fractography using convolutional neural networks & Rittel, D

Reference 7

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This paper cites V ., Berlanga, C.

Toward quantitative fractography using convolutional neural networks V ., Berlanga, C

Reference 8

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This paper cites & Molinari, J.

Toward quantitative fractography using convolutional neural networks & Molinari, J

Reference 9

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Toward quantitative fractography using convolutional neural networks & Srivastava, A

Reference 10

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This paper cites & Liu, L.

Toward quantitative fractography using convolutional neural networks & Liu, L

Reference 11

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This paper cites Y ., Student, O.

Toward quantitative fractography using convolutional neural networks Y ., Student, O

Reference 12

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This paper cites & Shoich, I.

Toward quantitative fractography using convolutional neural networks & Shoich, I

Reference 13

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Observation d8057bc4-a5c0-4fbb-8ac4-162c0751a500 · outbound

This paper cites Measurement: Journal of the International Measurement Confedera- tion 47, 130–144 (2014).

Toward quantitative fractography using convolutional neural networks Measurement: Journal of the International Measurement Confedera- tion 47, 130–144 (2014)

Reference 14

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Toward quantitative fractography using convolutional neural networks & Kobayashi, A

Reference 15

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This paper cites & Lewis, D.

Toward quantitative fractography using convolutional neural networks & Lewis, D

Reference 16

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Toward quantitative fractography using convolutional neural networks Unresolved cited work

Reference 18

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Toward quantitative fractography using convolutional neural networks Unresolved cited work

Reference 19

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Observation 1057b2b4-e9f5-4e0e-bf31-7ede0e9761ff · outbound

This paper cites X., Prieto-Ortiz, F.

Toward quantitative fractography using convolutional neural networks X., Prieto-Ortiz, F

Reference 20

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This paper cites & Juneviˇcius, R.

Toward quantitative fractography using convolutional neural networks & Juneviˇcius, R

Reference 21

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Observation 6dbbb7f8-f267-45dd-9d78-f6ad8446ea4f · outbound

This paper cites Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs.

Toward quantitative fractography using convolutional neural networks Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs

Reference 22

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This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

Toward quantitative fractography using convolutional neural networks Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 23

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Observation 1d9bde96-d015-4a8c-b834-48163d246ce2 · outbound

This paper cites Deep Residual Learning for Image Recognition.

Toward quantitative fractography using convolutional neural networks Deep Residual Learning for Image Recognition

Reference 24

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Observation 607ae6f8-dae4-4d20-81f1-66fa7fc2aa2c · outbound

This paper cites Going Deeper with Convolutions.

Toward quantitative fractography using convolutional neural networks Going Deeper with Convolutions

Reference 25

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Observation c8b1b32c-27fa-40ac-bc7a-2d6dff0fe70c · outbound

This paper cites DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition.

Toward quantitative fractography using convolutional neural networks DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition

Reference 26

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Observation 4a609f52-c6b9-45c2-b9bb-b59ae8b0acad · outbound

This paper cites Visualizing and Understanding Convolutional Networks.

Toward quantitative fractography using convolutional neural networks Visualizing and Understanding Convolutional Networks

Reference 27

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Toward quantitative fractography using convolutional neural networks Unresolved cited work

Reference 28

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This paper cites ImageNet Large Scale Visual Recognition Challenge.

Toward quantitative fractography using convolutional neural networks ImageNet Large Scale Visual Recognition Challenge

Reference 29

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Toward quantitative fractography using convolutional neural networks & Osovski, S

Reference 30

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This paper cites A Survey of Semantic Segmentation.

Toward quantitative fractography using convolutional neural networks A Survey of Semantic Segmentation

Reference 31

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Toward quantitative fractography using convolutional neural networks & Koller, D

Reference 32

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Toward quantitative fractography using convolutional neural networks & Criminisi, A

Reference 33

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This paper cites X-ray, lensing and Sunyaev Zel'dovich triaxial analysis of Abell 1835 out to R_{200}.

Toward quantitative fractography using convolutional neural networks X-ray, lensing and Sunyaev Zel'dovich triaxial analysis of Abell 1835 out to R_{200}

Reference 34

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Toward quantitative fractography using convolutional neural networks & Darrell, T

Reference 35

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Toward quantitative fractography using convolutional neural networks & Cipolla, R

Reference 36

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

Toward quantitative fractography using convolutional neural networks U-Net: Convolutional Networks for Biomedical Image Segmentation

Reference 37

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Toward quantitative fractography using convolutional neural networks & Others

Reference 38

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This paper cites TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems.

Toward quantitative fractography using convolutional neural networks TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems

Reference 39

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Toward quantitative fractography using convolutional neural networks Unresolved cited work

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Toward quantitative fractography using convolutional neural networks & Rittel, D

Reference 41

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Toward quantitative fractography using convolutional neural networks Unresolved cited work

Reference 42

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This paper cites The VIA Annotation Software for Images, Audio and Video.

Toward quantitative fractography using convolutional neural networks The VIA Annotation Software for Images, Audio and Video

Reference 43

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Toward quantitative fractography using convolutional neural networks & Zissermann, A

Reference 44

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Observation ccfc70f1-5c07-4d0f-bb9a-a77c336326ad · outbound

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Toward quantitative fractography using convolutional neural networks Adam: A Method for Stochastic Optimization

Reference 45

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