Paper Citation Record · LEDGER

Identifying and Compensating for Feature Deviation in Imbalanced Deep Learning

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2001.01385.

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

pith.paper-citation-record.v1
2001.01385 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 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 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T23:07:19.103691Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T23:06:19.587959Z

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 092c83a7-bff3-4c88-9480-cf4036f5ac18 · inbound

A Comprehensive Survey on Imbalanced Data Learning cites this paper.

A Comprehensive Survey on Imbalanced Data Learning Identifying and Compensating for Feature Deviation in Imbalanced Deep Learning

Reference 179

Resolution: unresolved; no resolver link, observed 2026-08-07T23:07:19.103691Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a25a28f1-ad0f-421f-9819-49ae1f63860f · inbound

Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts cites this paper.

Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts Identifying and Compensating for Feature Deviation in Imbalanced Deep Learning

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-06T18:52:23.230286Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d853b6a5-a23c-4f50-8211-f4287a8d41c6 · inbound

Measuring Weak-to-Strong Legibility of Reasoning Models cites this paper.

Measuring Weak-to-Strong Legibility of Reasoning Models Identifying and Compensating for Feature Deviation in Imbalanced Deep Learning

Reference 56

Resolution: unresolved; no resolver link, observed 2026-07-13T21:27:25.757351Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2cbffa5a-dac2-4dc6-a26c-92048e4c0922 · inbound

Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time cites this paper.

Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time Identifying and Compensating for Feature Deviation in Imbalanced Deep Learning

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-02T17:52:55.588814Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 31f3bbbc-0c85-4638-9438-e3cdebc75d3b · inbound

On the Impact of Class Imbalance on the Learning Dynamics of Deep Neural Networks:An Intuitive Insight cites this paper.

On the Impact of Class Imbalance on the Learning Dynamics of Deep Neural Networks:An Intuitive Insight Identifying and Compensating for Feature Deviation in Imbalanced Deep Learning

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-06-30T12:24:39.853777Z

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.

Observation 71420703-555d-4ca7-b27c-8618c601e4c9 · inbound

Why Not Hyperparameter-Friendly Optimisation? A Monotonic Adaptive Norm Rescaling Approach For Long-Tailed Recognition cites this paper.

Why Not Hyperparameter-Friendly Optimisation? A Monotonic Adaptive Norm Rescaling Approach For Long-Tailed Recognition Identifying and Compensating for Feature Deviation in Imbalanced Deep Learning

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-07-01T23:06:19.590292Z

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.