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Paper Citation Record · LEDGER

Improving Generalization of Transfer Learning Across Domains Using Spatio-Temporal Features in Autonomous Driving

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2103.08116.

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

pith.paper-citation-record.v1
2103.08116 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T19:55:09.879994Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T10:21:01.514352Z

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 e5777d03-5ff1-443a-a5f0-87f279d05695 · inbound

STLight: a Fully Convolutional Approach for Efficient Predictive Learning by Spatio-Temporal joint Processing cites this paper.

STLight: a Fully Convolutional Approach for Efficient Predictive Learning by Spatio-Temporal joint Processing Improving Generalization of Transfer Learning Across Domains Using Spatio-Temporal Features in Autonomous Driving

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-12T19:55:09.879994Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T19:55:09.879994Z digest=sha256:0f665c23ec2d7c034ebbf3b8fff0573bd6035adf63b6566060869464d7de01b1

Observation a2488373-541b-40ee-b492-1ed27fe35052 · inbound

HyperLiDAR: Adaptive Post-Deployment LiDAR Segmentation via Hyperdimensional Computing cites this paper.

HyperLiDAR: Adaptive Post-Deployment LiDAR Segmentation via Hyperdimensional Computing Improving Generalization of Transfer Learning Across Domains Using Spatio-Temporal Features in Autonomous Driving

Reference 2

Resolution
verified exact
arxiv_id, observed 2026-05-11T10:21:01.522971Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-10T15:32:26.359465Z digest=sha256:37f46edf6404bb26b24837fa00d4359966b2c3195c2f511a3662c2db0880c411