Paper Citation Record · LEDGER

HydraCIL: Decoupled Class-Incremental Learning through Prototype-Guided Multi-Head Classifiers

As of 9 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 0 inbound Pith citation observations for arXiv:2606.09960.

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

pith.paper-citation-record.v1
2606.09960 v1

Coverage vector

measured 18 of 18 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-27T17:03:01.884380Z

measured 18 of 18 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 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

18 of 18 outbound references displayed

verified exact2
verified fuzzy0
unresolved15
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 31a47617-98e9-4b42-97e7-3876dbcb8827 · outbound

This paper cites Class-incremental learning: A survey,.

HydraCIL: Decoupled Class-Incremental Learning through Prototype-Guided Multi-Head Classifiers Class-incremental learning: A survey,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-06-27T17:03:01.884380Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 28d5a02d-fe26-4e67-a695-acc9b44f3f4f · outbound

This paper cites Vision transformers on the edge: A comprehensive survey of model compression and acceleration strategies,.

HydraCIL: Decoupled Class-Incremental Learning through Prototype-Guided Multi-Head Classifiers Vision transformers on the edge: A comprehensive survey of model compression and acceleration strategies,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-06-27T17:03:01.884380Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 544a8da4-7d97-4497-91fc-c75aeb458fc2 · outbound

This paper cites Der: Dynamically expandable representation for class incremental learning,.

HydraCIL: Decoupled Class-Incremental Learning through Prototype-Guided Multi-Head Classifiers Der: Dynamically expandable representation for class incremental learning,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-06-27T17:03:01.884380Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 46b99321-b028-4e77-a80f-e6271be6b9ed · outbound

This paper cites Foster: Feature boosting and compression for class-incremental learning,.

HydraCIL: Decoupled Class-Incremental Learning through Prototype-Guided Multi-Head Classifiers Foster: Feature boosting and compression for class-incremental learning,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-06-27T17:03:01.884380Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 75d48079-5d3d-475b-9342-a39ce5ae61e6 · outbound

This paper cites A Model or 603 Exemplars: Towards Memory-Efficient Class-Incremental Learning.

HydraCIL: Decoupled Class-Incremental Learning through Prototype-Guided Multi-Head Classifiers A Model or 603 Exemplars: Towards Memory-Efficient Class-Incremental Learning

Reference 5

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T00:47:30.264532Z

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 65e402aa-f0d4-451a-b8cb-2b651d4cd89c · outbound

This paper cites Rmm: Reinforced memory manage- ment for class-incremental learning,.

HydraCIL: Decoupled Class-Incremental Learning through Prototype-Guided Multi-Head Classifiers Rmm: Reinforced memory manage- ment for class-incremental learning,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-06-27T17:03:01.884380Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 14e1ad62-bf22-482c-bf32-61eb72a63e63 · outbound

This paper cites Self-sustaining representation expansion for non-exemplar class-incremental learning,.

HydraCIL: Decoupled Class-Incremental Learning through Prototype-Guided Multi-Head Classifiers Self-sustaining representation expansion for non-exemplar class-incremental learning,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-06-27T17:03:01.884380Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation adb1f837-d20d-43e3-9400-b0efa5e001c5 · outbound

This paper cites Fetril: Feature translation for exemplar-free class-incremental learning,.

HydraCIL: Decoupled Class-Incremental Learning through Prototype-Guided Multi-Head Classifiers Fetril: Feature translation for exemplar-free class-incremental learning,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-06-27T17:03:01.884380Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 937750b8-cfd1-4deb-a426-613fadcefced · outbound

This paper cites Representation robustness and feature expansion for exemplar-free class-incremental learning,.

HydraCIL: Decoupled Class-Incremental Learning through Prototype-Guided Multi-Head Classifiers Representation robustness and feature expansion for exemplar-free class-incremental learning,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-06-27T17:03:01.884380Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d87bbb31-110c-4970-841c-600e602f4465 · outbound

This paper cites How efficient are today’s continual learning algorithms?.

HydraCIL: Decoupled Class-Incremental Learning through Prototype-Guided Multi-Head Classifiers How efficient are today’s continual learning algorithms?

Reference 10

Resolution: unresolved; no resolver link, observed 2026-06-27T17:03:01.884380Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b4103cf1-23b5-4659-ba1c-aefa44830495 · outbound

This paper cites Remind your neural network to prevent catastrophic forgetting,.

HydraCIL: Decoupled Class-Incremental Learning through Prototype-Guided Multi-Head Classifiers Remind your neural network to prevent catastrophic forgetting,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-06-27T17:03:01.884380Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 396b5822-c3cb-4c9f-9c84-cc78ea12db35 · outbound

This paper cites CIFNet: An Analytic Neural Learning Framework for Efficient and Calibrated Class-Incremental Learning.

HydraCIL: Decoupled Class-Incremental Learning through Prototype-Guided Multi-Head Classifiers CIFNet: An Analytic Neural Learning Framework for Efficient and Calibrated Class-Incremental Learning

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-07-29T01:24:25.241578Z

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 c83a978c-cb88-4cd4-b1a9-7ef9ef120a5b · outbound

This paper cites 2024.mlco2/codecarbon: v2.4.1.

HydraCIL: Decoupled Class-Incremental Learning through Prototype-Guided Multi-Head Classifiers 2024.mlco2/codecarbon: v2.4.1

Reference 13

Resolution: verified exact; doi, observed 2026-06-27T17:11:05.665907Z

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 d70b176c-09f9-4fdd-9798-42066e0b1d4e · outbound

This paper cites Imagenet: A large-scale hierarchical image database,.

HydraCIL: Decoupled Class-Incremental Learning through Prototype-Guided Multi-Head Classifiers Imagenet: A large-scale hierarchical image database,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-06-27T17:03:01.884380Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0b068667-c214-49a1-bc38-50563c2cc931 · outbound

This paper cites Learning multiple layers of features from tiny images,.

HydraCIL: Decoupled Class-Incremental Learning through Prototype-Guided Multi-Head Classifiers Learning multiple layers of features from tiny images,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-06-27T17:03:01.884380Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 580c6933-bf67-485d-920d-7a6eb12323c7 · outbound

This paper cites Core50: a new dataset and benchmark for continuous object recognition,.

HydraCIL: Decoupled Class-Incremental Learning through Prototype-Guided Multi-Head Classifiers Core50: a new dataset and benchmark for continuous object recognition,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-06-27T17:03:01.884380Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b310f307-291f-4218-8cee-e175bb32dc48 · outbound

This paper cites Automated flower classification over a large number of classes,.

HydraCIL: Decoupled Class-Incremental Learning through Prototype-Guided Multi-Head Classifiers Automated flower classification over a large number of classes,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-06-27T17:03:01.884380Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1036f25e-41f2-48f3-8441-ce2d071bddd7 · outbound

This paper cites Fast and frugal transfer learning via precomputed features and adaptive normal- ization,.

HydraCIL: Decoupled Class-Incremental Learning through Prototype-Guided Multi-Head Classifiers Fast and frugal transfer learning via precomputed features and adaptive normal- ization,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-06-27T17:03:01.884380Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.