Paper Citation Record · LEDGER

Class-Incremental Learning: A Survey

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2302.03648.

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

pith.paper-citation-record.v1
2302.03648 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 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 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:28:47.792974Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T18:08:18.770237Z

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 4f42bd2a-4a59-4661-a717-5df000364357 · inbound

Sparse Orthogonal Parameters Tuning for Continual Learning cites this paper.

Sparse Orthogonal Parameters Tuning for Continual Learning Class-Incremental Learning: A Survey

Reference 39

Resolution: verified exact; arxiv_id, observed 2026-05-23T18:08:18.773154Z

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 aee6a4fd-7441-421d-805a-289293e03f22 · inbound

A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning cites this paper.

A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning Class-Incremental Learning: A Survey

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T15:28:47.792974Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7fb11f28-8343-4501-a4ab-25a4c831dd02 · inbound

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

Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts Class-Incremental Learning: A Survey

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-06T18:52:23.274480Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0e6fba71-e5cb-48f6-be32-dee50deaa41f · inbound

C-Flat++: Towards a More Efficient and Powerful Framework for Continual Learning cites this paper.

C-Flat++: Towards a More Efficient and Powerful Framework for Continual Learning Class-Incremental Learning: A Survey

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-05T16:16:46.479241Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e00a20e0-0cf6-4c89-885c-946893093764 · inbound

Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labels cites this paper.

Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labels Class-Incremental Learning: A Survey

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-05T14:25:59.271945Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1659b2f8-c1cc-4897-a568-450421948b70 · inbound

A Faster Path to Continual Learning cites this paper.

A Faster Path to Continual Learning Class-Incremental Learning: A Survey

Reference 66

Resolution: verified exact; arxiv_id, observed 2026-05-11T10:41:02.566040Z

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 06b29e7c-f145-4a91-bac8-c307652923ed · inbound

UniAlign: A Model-Agnostic Framework for Robust Network Traffic Classification under Distribution Shifts cites this paper.

UniAlign: A Model-Agnostic Framework for Robust Network Traffic Classification under Distribution Shifts Class-Incremental Learning: A Survey

Reference 66

Resolution: verified exact; arxiv_id, observed 2026-05-20T13:33:18.926093Z

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.