Paper Citation Record · LEDGER

Customizing Student Networks From Heterogeneous Teachers via Adaptive Knowledge Amalgamation

As of 20 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:1908.07121.

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

pith.paper-citation-record.v1
1908.07121 v1

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T12:30:18.298366Z

measured 37 of 37 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 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

37 of 37 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 827fad7e-de2b-4163-aecf-4e49302af8f0 · outbound

This paper cites Segnet: A deep convolutional encoder-decoder architecture for image segmentation.

Customizing Student Networks From Heterogeneous Teachers via Adaptive Knowledge Amalgamation Segnet: A deep convolutional encoder-decoder architecture for image segmentation

Reference 1

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Observation 537234c3-e881-4ae6-8a84-2fbb52c831af · outbound

This paper cites Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolu- tion, and fully connected crfs.

Customizing Student Networks From Heterogeneous Teachers via Adaptive Knowledge Amalgamation Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolu- tion, and fully connected crfs

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:30:18.872741Z

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Observation 231d4eb9-cbb5-4881-b212-86bcd6350101 · outbound

This paper cites Large scale ﬁne-grained categorization and domain-speciﬁc transfer learning.

Customizing Student Networks From Heterogeneous Teachers via Adaptive Knowledge Amalgamation Large scale ﬁne-grained categorization and domain-speciﬁc transfer learning

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:30:18.858436Z

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Observation 125f6045-9643-4abd-b696-cdbc670d586d · outbound

This paper cites Graph adaptive knowledge transfer for unsupervised domain adaptation.

Customizing Student Networks From Heterogeneous Teachers via Adaptive Knowledge Amalgamation Graph adaptive knowledge transfer for unsupervised domain adaptation

Reference 4

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Observation 5a6ec732-7a13-41fa-8e42-da35c3dd5d14 · outbound

This paper cites Punda: Probabilistic unsupervised domain adaptation for knowledge transfer across visual categories.

Customizing Student Networks From Heterogeneous Teachers via Adaptive Knowledge Amalgamation Punda: Probabilistic unsupervised domain adaptation for knowledge transfer across visual categories

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:30:18.829410Z

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Observation 62e89c43-2dbd-425d-9e39-784eb823988d · outbound

This paper cites Deep residual learning for image recognition.

Customizing Student Networks From Heterogeneous Teachers via Adaptive Knowledge Amalgamation Deep residual learning for image recognition

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:30:18.814616Z

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Observation ead3bcf4-e16d-4ab5-91eb-2579e57b60ef · outbound

This paper cites Multi-task zip- ping via layer-wise neuron sharing.

Customizing Student Networks From Heterogeneous Teachers via Adaptive Knowledge Amalgamation Multi-task zip- ping via layer-wise neuron sharing

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:30:18.799864Z

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Observation a2d7d2ab-4bea-484c-ab52-9642c9e4bb7b · outbound

This paper cites Distilling the knowledge in a neural network.

Customizing Student Networks From Heterogeneous Teachers via Adaptive Knowledge Amalgamation Distilling the knowledge in a neural network

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:30:18.785114Z

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Observation ea2b197e-00fa-4f89-b4c4-e6e26bb783e5 · outbound

This paper cites Learning transferrable knowledge for semantic seg- mentation with deep convolutional neural network.

Customizing Student Networks From Heterogeneous Teachers via Adaptive Knowledge Amalgamation Learning transferrable knowledge for semantic seg- mentation with deep convolutional neural network

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:30:18.770654Z

Source-reported events for the cited work

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Observation 78cde3d4-3a9b-4167-9d04-7e132087dd88 · outbound

This paper cites Deep transfer met- ric learning.

Customizing Student Networks From Heterogeneous Teachers via Adaptive Knowledge Amalgamation Deep transfer met- ric learning

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:30:18.756434Z

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 a8744ed3-8f47-4368-9efc-7b1d5d870c4f · outbound

This paper cites Densely connected convolutional networks.

Customizing Student Networks From Heterogeneous Teachers via Adaptive Knowledge Amalgamation Densely connected convolutional networks

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:30:18.742502Z

Source-reported events for the cited work

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Observation 9284d00a-4b3a-42f0-8327-39dadee7cbed · outbound

This paper cites Domain transfer through deep activation matching.

Customizing Student Networks From Heterogeneous Teachers via Adaptive Knowledge Amalgamation Domain transfer through deep activation matching

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:30:18.728017Z

Source-reported events for the cited work

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Observation 0b82554e-4e5e-4ce0-b4e9-18f99817eaaa · outbound

This paper cites Like What You Like: Knowledge Distill via Neuron Selectivity Transfer.

Customizing Student Networks From Heterogeneous Teachers via Adaptive Knowledge Amalgamation Like What You Like: Knowledge Distill via Neuron Selectivity Transfer

Reference 13

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Observation bbd5251e-2f2d-487e-99f6-57b0b9dfdd51 · outbound

This paper cites Knowledge ﬂow: Improve upon your teachers.

Customizing Student Networks From Heterogeneous Teachers via Adaptive Knowledge Amalgamation Knowledge ﬂow: Improve upon your teachers

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:30:18.712850Z

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Observation 9ecf8f65-c409-4f5e-b3d0-a326435958d5 · outbound

This paper cites Novel dataset for ﬁne-grained image categorization.

Customizing Student Networks From Heterogeneous Teachers via Adaptive Knowledge Amalgamation Novel dataset for ﬁne-grained image categorization

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:30:18.697578Z

Source-reported events for the cited work

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Observation db37dbb7-3b74-4022-a6aa-05f213003c35 · outbound

This paper cites 3d object representations for ﬁne-grained categorization.

Customizing Student Networks From Heterogeneous Teachers via Adaptive Knowledge Amalgamation 3d object representations for ﬁne-grained categorization

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:30:18.683250Z

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

Observation 564ea13e-5292-4067-9201-ab75d289a059 · outbound

This paper cites Imagenet classiﬁcation with deep convolutional neural net- works.

Customizing Student Networks From Heterogeneous Teachers via Adaptive Knowledge Amalgamation Imagenet classiﬁcation with deep convolutional neural net- works

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:30:18.669047Z

Source-reported events for the cited work

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Observation 7d7012c8-fa69-4f31-b12d-27df3ffe00e1 · outbound

This paper cites Few Sample Knowledge Distillation for Efficient Network Compression.

Customizing Student Networks From Heterogeneous Teachers via Adaptive Knowledge Amalgamation Few Sample Knowledge Distillation for Efficient Network Compression

Reference 18

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Observation 564f911d-0cb0-48a9-9ca3-efc6dccbf446 · outbound

This paper cites Ssd: Single shot multibox detector.

Customizing Student Networks From Heterogeneous Teachers via Adaptive Knowledge Amalgamation Ssd: Single shot multibox detector

Reference 19

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Observation 2a0828b9-7b9c-42bd-808f-dabc355e1ed9 · outbound

This paper cites Deep learning face attributes in the wild.

Customizing Student Networks From Heterogeneous Teachers via Adaptive Knowledge Amalgamation Deep learning face attributes in the wild

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:30:18.639135Z

Source-reported events for the cited work

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Observation 041f66c6-cc1f-463e-b2aa-6b6dbf5ae258 · outbound

This paper cites Fully convolutional networks for semantic segmentation.

Customizing Student Networks From Heterogeneous Teachers via Adaptive Knowledge Amalgamation Fully convolutional networks for semantic segmentation

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:30:18.624214Z

Source-reported events for the cited work

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Observation 29753d80-3bd9-49ff-9a93-a990a3e9539b · outbound

This paper cites Transfer feature learning with joint dis- tribution adaptation.

Customizing Student Networks From Heterogeneous Teachers via Adaptive Knowledge Amalgamation Transfer feature learning with joint dis- tribution adaptation

Reference 22

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Source-reported events for the cited work

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Observation a7571f1a-94ec-4696-842f-e0a34f83ae29 · outbound

This paper cites Knowledge amalgamation from het- erogeneous networks by common feature learning.

Customizing Student Networks From Heterogeneous Teachers via Adaptive Knowledge Amalgamation Knowledge amalgamation from het- erogeneous networks by common feature learning

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:30:18.593057Z

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Observation a5e55a66-5d98-46d5-8c22-0d6e5d7941e7 · outbound

This paper cites A survey on transfer learning.

Customizing Student Networks From Heterogeneous Teachers via Adaptive Knowledge Amalgamation A survey on transfer learning

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:30:18.575654Z

Source-reported events for the cited work

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Observation c3c54dc8-9f89-40bf-b397-6f735f40da1a · outbound

This paper cites Faster r-cnn: Towards real-time object detection with region proposal networks.

Customizing Student Networks From Heterogeneous Teachers via Adaptive Knowledge Amalgamation Faster r-cnn: Towards real-time object detection with region proposal networks

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:30:18.560464Z

Source-reported events for the cited work

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Observation 58ea6fdc-ca66-4598-993a-36cf0b58b25c · outbound

This paper cites Fit- nets: Hints for thin deep nets.

Customizing Student Networks From Heterogeneous Teachers via Adaptive Knowledge Amalgamation Fit- nets: Hints for thin deep nets

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:30:18.544203Z

Source-reported events for the cited work

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Observation a1abedce-e4d6-42d0-bd06-ed2f3b23d7db · outbound

This paper cites Fine-Grained Visual Classification of Aircraft.

Customizing Student Networks From Heterogeneous Teachers via Adaptive Knowledge Amalgamation Fine-Grained Visual Classification of Aircraft

Reference 27

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This paper cites Amalgamating knowledge towards comprehensive classiﬁcation.

Customizing Student Networks From Heterogeneous Teachers via Adaptive Knowledge Amalgamation Amalgamating knowledge towards comprehensive classiﬁcation

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:30:18.528844Z

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Observation 7a91aed5-67d4-441d-ae46-62b6ce346358 · outbound

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

Customizing Student Networks From Heterogeneous Teachers via Adaptive Knowledge Amalgamation Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 29

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Customizing Student Networks From Heterogeneous Teachers via Adaptive Knowledge Amalgamation Going deeper with convolutions

Reference 30

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Observation 9e92ff37-1073-4c75-811e-4574c76aa489 · outbound

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Customizing Student Networks From Heterogeneous Teachers via Adaptive Knowledge Amalgamation Unresolved cited work

Reference 31

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This paper cites Accel- erating convolutional neural networks with dominant convo- lutional kernel and knowledge pre-regression.

Customizing Student Networks From Heterogeneous Teachers via Adaptive Knowledge Amalgamation Accel- erating convolutional neural networks with dominant convo- lutional kernel and knowledge pre-regression

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:30:18.486536Z

Source-reported events for the cited work

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Observation f639c9e8-3582-4ee1-a76a-9e93987819f9 · outbound

This paper cites Student becoming the master: Knowledge amalgamation for joint scene parsing, depth estimation, and more.

Customizing Student Networks From Heterogeneous Teachers via Adaptive Knowledge Amalgamation Student becoming the master: Knowledge amalgamation for joint scene parsing, depth estimation, and more

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:30:18.470158Z

Source-reported events for the cited work

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Observation a81f4317-0797-4d31-a336-cf6979505d16 · outbound

This paper cites Amalgamating ﬁltered knowledge: Learning task- customized student from multi-task teachers.

Customizing Student Networks From Heterogeneous Teachers via Adaptive Knowledge Amalgamation Amalgamating ﬁltered knowledge: Learning task- customized student from multi-task teachers

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:30:18.454171Z

Source-reported events for the cited work

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Observation a1f04295-f0d0-484f-8f2f-3ebfe007dfdb · outbound

This paper cites Learning from multiple teacher networks.

Customizing Student Networks From Heterogeneous Teachers via Adaptive Knowledge Amalgamation Learning from multiple teacher networks

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:30:18.438679Z

Source-reported events for the cited work

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Observation 87a05a8c-20b8-4c99-afa1-2f8c49b1b19a · outbound

This paper cites Paying more at- tention to attention: Improving the performance of convolu- tional neural networks via attention transfer.

Customizing Student Networks From Heterogeneous Teachers via Adaptive Knowledge Amalgamation Paying more at- tention to attention: Improving the performance of convolu- tional neural networks via attention transfer

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:30:18.423570Z

Source-reported events for the cited work

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Observation 06c179ec-4920-40bb-afd8-0a67884f65c7 · outbound

This paper cites Single-shot reﬁnement neural network for ob- ject detection.

Customizing Student Networks From Heterogeneous Teachers via Adaptive Knowledge Amalgamation Single-shot reﬁnement neural network for ob- ject detection

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:30:18.407312Z

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.

Pith citing papers

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