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Paper Citation Record · LEDGER

Transferability in Deep Learning: A Survey

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2201.05867.

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

pith.paper-citation-record.v1
2201.05867 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 standing notices

One-hop event checks from named stored sources.

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

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:30:21.837380Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T08:46:49.150026Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 3a97599f-fc37-40f3-847c-62b955495deb · inbound

Human Heterogeneity Invariant Stress Sensing cites this paper.

Human Heterogeneity Invariant Stress Sensing Transferability in Deep Learning: A Survey

Reference 56

Resolution
unresolved
no resolver link, observed 2026-08-07T11:30:21.837380Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:30:21.837380Z digest=sha256:2f40349dc9eaae401882592d18c295ae2365c0326a2da1e67ebd79de4c42b1a9

Observation 0b87e224-db3d-474f-a907-5a4891119eeb · inbound

Understanding Knowledge Transferability for Transfer Learning: A Survey cites this paper.

Understanding Knowledge Transferability for Transfer Learning: A Survey Transferability in Deep Learning: A Survey

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-06T20:22:25.434715Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:22:25.434715Z digest=sha256:46fb9fd0016071f9f62aec54aab23e1af13a6ba22b55018bbd0a76b6986de0ae

Observation 42b4bf2e-b14f-4832-9c96-ba2eaaa6ce52 · inbound

Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and Projection cites this paper.

Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and Projection Transferability in Deep Learning: A Survey

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-06T19:21:28.546983Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:21:28.546983Z digest=sha256:fd47b4075bff28a6ebcc64d4a598ed11f2dd75efca75c72783236cb18758e4e6

Observation 4f69a97e-d8a1-4837-adcf-1fa75c45c9e9 · inbound

From Time-series Generation, Model Selection to Transfer Learning: A Comparative Review of Pixel-wise Approaches for Large-scale Crop Mapping cites this paper.

From Time-series Generation, Model Selection to Transfer Learning: A Comparative Review of Pixel-wise Approaches for Large-scale Crop Mapping Transferability in Deep Learning: A Survey

Reference 36

Resolution
verified exact
arxiv_id, observed 2026-05-19T03:57:02.997851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-19T03:55:23.526772Z digest=sha256:0186b9008999fe28882668d8f9c98b146eb20cc9052d426488b1ff239e40d721

Observation 2818b661-57a6-4623-8fe0-c043728f4c0d · inbound

AI in Agriculture: A Survey of Deep Learning Techniques for Crops, Fisheries and Livestock cites this paper.

AI in Agriculture: A Survey of Deep Learning Techniques for Crops, Fisheries and Livestock Transferability in Deep Learning: A Survey

Reference 287

Resolution
metadata mismatch
arxiv_id, observed 2026-05-19T02:06:58.776994Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-19T02:03:46.331803Z digest=sha256:fbb9b3054787d5388856d8917e090642f5c9bdb175c7a22d7c1037f964b5780a

Observation 62d26a4c-5b9b-40d6-bf9e-7c3af3f5f463 · inbound

rETF-semiSL: Semi-Supervised Learning for Neural Collapse in Temporal Data cites this paper.

rETF-semiSL: Semi-Supervised Learning for Neural Collapse in Temporal Data Transferability in Deep Learning: A Survey

Reference 3830

Resolution
unresolved
no resolver link, observed 2026-08-05T20:44:38.126034Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T20:44:38.126034Z digest=sha256:9a15811b53473e0a308c37ad6434ed1d3271beaeb08fd51f020e56c867c7a236

Observation b0e1c3f9-a98e-4b88-9cd3-f04688688076 · inbound

Towards Realistic Hand-Object Interaction with Gravity-Field Based Diffusion Bridge cites this paper.

Towards Realistic Hand-Object Interaction with Gravity-Field Based Diffusion Bridge Transferability in Deep Learning: A Survey

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-05T11:12:14.545830Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T11:12:14.545830Z digest=sha256:b09ca587343fe27e3a86986403cdfeb44d616086c042a934e9387738efa83307

Observation 938627cf-50a8-4308-b79c-e1acf9490f73 · inbound

RADAR: Relative Angular Divergence Across Representations cites this paper.

RADAR: Relative Angular Divergence Across Representations Transferability in Deep Learning: A Survey

Reference 21

Resolution
verified exact
arxiv_id, observed 2026-05-25T05:40:23.917574Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-25T05:39:39.765999Z digest=sha256:b13cfd86bb816820b1479b30030a090f3e236c1499f0116cd823c06724ba9c0b

Observation 512ca432-4cd1-477a-a606-96fb850dee62 · inbound

Semi-Supervised Noise Adaptation: Transferring Knowledge from Noise Domain cites this paper.

Semi-Supervised Noise Adaptation: Transferring Knowledge from Noise Domain Transferability in Deep Learning: A Survey

Reference 2

Resolution
verified exact
arxiv_id, observed 2026-06-28T19:32:34.903210Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-28T19:29:34.901868Z digest=sha256:9b07f986143c43056629ff90a5ec98475ca1ff5317765523a00c87a8142c140f

Observation e0a7ddf0-42e9-48b7-a24f-4e997d26c0b7 · inbound

X4Val: Learning Neural Surrogates for Variance-Reduced Policy Evaluation cites this paper.

X4Val: Learning Neural Surrogates for Variance-Reduced Policy Evaluation Transferability in Deep Learning: A Survey

Reference 11

Resolution
verified exact
arxiv_id, observed 2026-07-02T08:46:49.151343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-28T05:45:37.788703Z digest=sha256:0a7f1ae7cfb05fd0eb06211eb7c6a88be9b23e3e13b698e056233c1f7d126650