Paper Citation Record · LEDGER

Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need

As of 19 August 2026, this Paper Citation Record lists 61 of 61 outbound references and 2 inbound Pith citation observations for arXiv:2505.23744.

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

pith.paper-citation-record.v1
2505.23744 v1

Coverage vector

measured 61 of 61 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:43:32.108846Z

measured 63 of 63 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T09:27:52.611645Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T06:34:19.417847Z

Reference resolution

61 of 61 outbound references displayed

verified exact5
verified fuzzy39
unresolved17
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 19738e5a-d37d-400d-820d-6df17217fb19 · outbound

This paper cites Subspace Regularizers for Few-Shot Class Incremental Learning.

Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need Subspace Regularizers for Few-Shot Class Incremental Learning

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-08-07T12:43:33.212776Z

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Observation 2fb12213-a278-4464-a0b4-b803028a80d3 · outbound

This paper cites Memory aware synapses: Learning what (not) to forget.

Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need Memory aware synapses: Learning what (not) to forget

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:41.257847Z

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Observation 429ded33-9ee2-43b4-8d10-32b5c49b77f5 · outbound

This paper cites Leaving none behind: Data-free domain incremental learn- ing for major depressive disorder detection.

Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need Leaving none behind: Data-free domain incremental learn- ing for major depressive disorder detection

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:41.022004Z

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Observation f22d20ef-e02b-4b4c-ba7b-8170798e26db · outbound

This paper cites The cityscapes dataset for semantic urban scene understanding.

Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need The cityscapes dataset for semantic urban scene understanding

Reference 4

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Observation 6ea6480e-d7e0-4cca-81e8-0014b8c783b9 · outbound

This paper cites Space Rotation with Basis Transformation for Training-free Test-Time Adaptation.

Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need Space Rotation with Basis Transformation for Training-free Test-Time Adaptation

Reference 5

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Observation bd87593f-9da4-489e-87ba-96e32dd49b0e · outbound

This paper cites Domain incremental object detection based on feature space topology preserv- ing strategy.

Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need Domain incremental object detection based on feature space topology preserv- ing strategy

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:40.832247Z

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Observation c1f1a819-65af-4b3b-9e2e-ab96ed10d042 · outbound

This paper cites Knowledge restore and transfer for multi-label class-incremental learning.

Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need Knowledge restore and transfer for multi-label class-incremental learning

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:40.604506Z

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Observation cb9f05a0-49f8-4b88-9af7-acffec83d97b · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 8

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Observation 4768b742-b44a-472f-86ed-dd18cc928a58 · outbound

This paper cites Dytox: Transformers for continual learning with dynamic token expansion.

Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need Dytox: Transformers for continual learning with dynamic token expansion

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:40.364905Z

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Observation 88596698-6431-4612-944b-7591c3b7db05 · outbound

This paper cites The pascal visual object classes (voc) challenge.

Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need The pascal visual object classes (voc) challenge

Reference 10

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Observation 8d1c3972-ffc4-45af-a5c6-567773e952b3 · outbound

This paper cites Overcoming catastrophic forgetting in incremental object detection via elastic response distillation.

Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need Overcoming catastrophic forgetting in incremental object detection via elastic response distillation

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:40.178529Z

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Observation 7b8e2e6c-35ce-4554-a3c6-9c79c7eba977 · outbound

This paper cites Beyond prompt learning: Continual adapter for efficient rehearsal-free continual learning.

Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need Beyond prompt learning: Continual adapter for efficient rehearsal-free continual learning

Reference 12

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Observation d6f6c8e4-c5a5-49e5-99ca-2973ac720965 · outbound

This paper cites Multi-domain incremental learning for semantic segmenta- tion.

Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need Multi-domain incremental learning for semantic segmenta- tion

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:39.811871Z

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Observation 322e3a5b-a195-436e-92d7-0b0500daad81 · outbound

This paper cites Csr-i (wsj0) complete ldc93s6a.

Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need Csr-i (wsj0) complete ldc93s6a

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:39.689487Z

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Observation 7f997b53-9489-48b3-8758-eaae75fd8ed9 · outbound

This paper cites Learn by reasoning: Analogical weight generation for few-shot class- incremental learning.

Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need Learn by reasoning: Analogical weight generation for few-shot class- incremental learning

Reference 15

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Observation 6b2fada7-ffaf-466d-8c8c-c048f1334696 · outbound

This paper cites An end-to- end architecture for class-incremental object detection with knowledge distillation.

Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need An end-to- end architecture for class-incremental object detection with knowledge distillation

Reference 16

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Observation 5f9adb09-f2f6-4035-a7c0-dd7801bbbf87 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need Distilling the Knowledge in a Neural Network

Reference 17

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Observation 52e9eeac-0900-4ba9-9dbf-0e168cd25d45 · outbound

This paper cites Learning a unified classifier incrementally via rebalancing.

Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need Learning a unified classifier incrementally via rebalancing

Reference 18

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Observation d8bbd382-4c63-42d0-bfa3-87a2c7aaea6b · outbound

This paper cites Depth-attentional features for single-image rain removal.

Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need Depth-attentional features for single-image rain removal

Reference 19

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Observation d3a8291a-c069-4fc8-b37b-8fd7e5603944 · outbound

This paper cites Minimum class confusion for versatile domain adaptation.

Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need Minimum class confusion for versatile domain adaptation

Reference 20

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Observation 5f865fa3-fc6c-4e04-b269-44da31e85246 · outbound

This paper cites Class- incremental learning by knowledge distillation with adaptive feature consolidation.

Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need Class- incremental learning by knowledge distillation with adaptive feature consolidation

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:39.298069Z

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Observation b353d1c5-09ae-4973-8a2f-c395298c7c69 · outbound

This paper cites Overcoming catastrophic forgetting in neu- ral networks.

Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need Overcoming catastrophic forgetting in neu- ral networks

Reference 22

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Observation e1cf589b-d20a-414e-86bb-0c77d89c4ffc · outbound

This paper cites Clustering- based domain-incremental learning.

Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need Clustering- based domain-incremental learning

Reference 23

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Observation a6650cc4-6dbe-4af0-a344-568c9c4caa65 · outbound

This paper cites SERIL: Noise Adaptive Speech Enhancement using Regularization-based Incremental Learning.

Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need SERIL: Noise Adaptive Speech Enhancement using Regularization-based Incremental Learning

Reference 24

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Observation dd0db7c4-b061-4ac8-8af2-663752632f1e · outbound

This paper cites A continual deepfake detection benchmark: Dataset, meth- ods, and essentials.

Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need A continual deepfake detection benchmark: Dataset, meth- ods, and essentials

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:38.902111Z

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Observation 8a8b506b-1185-4216-ad94-8ed00f77718f · outbound

This paper cites Learning from students: Online contrastive distillation net- work for general continual learning.

Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need Learning from students: Online contrastive distillation net- work for general continual learning

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:38.745924Z

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Observation f99097e5-3b5c-44df-b62c-af6fbb31ca4f · outbound

This paper cites Learning without forgetting.

Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need Learning without forgetting

Reference 27

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Observation 65624bac-ea85-4f1f-b852-2cee131bc2c3 · outbound

This paper cites Few-shot class-incremental learning via entropy-regularized data-free replay.

Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need Few-shot class-incremental learning via entropy-regularized data-free replay

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:38.260590Z

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Observation 122d3e32-c2e1-41ac-9903-8ae19e0d6870 · outbound

This paper cites Multi-Task Incremental Learning for Object Detection.

Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need Multi-Task Incremental Learning for Object Detection

Reference 29

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Observation 8050d547-6107-4dd1-a001-0fe9ecea30f0 · outbound

This paper cites Continual detection transformer for incremen- tal object detection.

Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need Continual detection transformer for incremen- tal object detection

Reference 30

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Observation efedbc5e-51b0-4b0e-a283-a3df60b37b3d · outbound

This paper cites Compositional prompting for anti-forgetting in domain incremental learn- ing.

Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need Compositional prompting for anti-forgetting in domain incremental learn- ing

Reference 31

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Observation 57ad4f06-59ee-45f9-aff9-e6989daeb2cb · outbound

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

Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need Core50: a new dataset and benchmark for continuous object recognition

Reference 32

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Observation d6bd8656-5d37-4cad-9385-deba4d8332e5 · outbound

This paper cites Mop-clip: A mixture of prompt-tuned clip models for domain incremental learning.

Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need Mop-clip: A mixture of prompt-tuned clip models for domain incremental learning

Reference 33

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Observation 8156b5cc-4ae8-42b4-9db4-0f4fb4bd80a2 · outbound

This paper cites Moment matching for multi-source domain adaptation.

Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need Moment matching for multi-source domain adaptation

Reference 34

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Observation 8d8390c2-7103-4775-996c-8ca36625f0be · outbound

This paper cites Learning transferable visual models from natural language supervi- sion.

Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need Learning transferable visual models from natural language supervi- sion

Reference 35

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This paper cites icarl: Incremental classifier and representation learning.

Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need icarl: Incremental classifier and representation learning

Reference 36

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This paper cites A unified approach to do- main incremental learning with memory: Theory and algo- rithm.

Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need A unified approach to do- main incremental learning with memory: Theory and algo- rithm

Reference 37

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Observation 6cfce464-0401-4a1e-9645-c3b47b858963 · outbound

This paper cites Multi-granularity knowl- edge distillation and prototype consistency regularization for class-incremental learning.

Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need Multi-granularity knowl- edge distillation and prototype consistency regularization for class-incremental learning

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:36.747111Z

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Observation 8be7c51d-026f-4a2c-80cc-0bea41ef56f2 · outbound

This paper cites Coda-prompt: Contin- ual decomposed attention-based prompting for rehearsal-free continual learning.

Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need Coda-prompt: Contin- ual decomposed attention-based prompting for rehearsal-free continual learning

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:36.497967Z

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Observation 14f24ee8-f6cc-47b1-b4f2-1a4ad3c16153 · outbound

This paper cites Non-exemplar domain incremental object detection via learning domain bias.

Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need Non-exemplar domain incremental object detection via learning domain bias

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:36.304745Z

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Observation 3f308b2b-798c-4bed-acb8-166b799bad4f · outbound

This paper cites Overcoming catastrophic forgetting for multi-label class-incremental learning.

Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need Overcoming catastrophic forgetting for multi-label class-incremental learning

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:36.025542Z

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Observation 013f21f5-6cb5-4012-96d9-9c1c501208d1 · outbound

This paper cites Assessment for automatic speech recognition: Ii.

Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need Assessment for automatic speech recognition: Ii

Reference 42

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Observation 30264977-add5-490e-ba82-bb91fe68a94f · outbound

This paper cites Instance rela- tion graph guided source-free domain adaptive object detec- tion.

Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need Instance rela- tion graph guided source-free domain adaptive object detec- tion

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:35.718006Z

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Observation c5e2bae0-6269-4eb3-8f37-d32194b78a7c · outbound

This paper cites Multi-domain incremental learning for face presentation attack detection.

Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need Multi-domain incremental learning for face presentation attack detection

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:35.574672Z

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Observation 457edd89-7316-4ec8-a2a3-50ede4ab241f · outbound

This paper cites Non-exemplar domain incremental learning via cross-domain concept integration.

Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need Non-exemplar domain incremental learning via cross-domain concept integration

Reference 45

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Observation 5a7f9031-8a4f-42e8-8500-9e883106bc47 · outbound

This paper cites DualCP: Rehearsal-Free Domain-Incremental Learning via Dual-Level Concept Prototype.

Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need DualCP: Rehearsal-Free Domain-Incremental Learning via Dual-Level Concept Prototype

Reference 46

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Observation 0faa8fe5-8913-4fb5-95a7-fa4cd6832897 · outbound

This paper cites Importance-aware shared param- eter subspace learning for domain incremental learning.

Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need Importance-aware shared param- eter subspace learning for domain incremental learning

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:35.292600Z

Source-reported events for the cited work

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Observation d5b19ea4-8fc5-4369-8fda-d1fe444d6e9e · outbound

This paper cites S-prompts learning with pre-trained transformers: An occam’s razor for domain incremental learning.

Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need S-prompts learning with pre-trained transformers: An occam’s razor for domain incremental learning

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:35.151481Z

Source-reported events for the cited work

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Observation c4067498-a8f5-4286-bbb7-16bdb907e6c0 · outbound

This paper cites Isolation and impartial aggre- gation: A paradigm of incremental learning without interfer- ence.

Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need Isolation and impartial aggre- gation: A paradigm of incremental learning without interfer- ence

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:34.939018Z

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Observation c8fec301-8538-42dc-a4cc-c60923d49769 · outbound

This paper cites Dualprompt: Complementary prompting for rehearsal-free continual learning.

Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need Dualprompt: Complementary prompting for rehearsal-free continual learning

Reference 50

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Observation 412198a5-503c-44b9-8219-e5ce0fbee976 · outbound

This paper cites Learning to prompt for continual learning.

Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need Learning to prompt for continual learning

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-07T12:43:31.545880Z

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Observation 83c20595-caa4-485c-867c-66f6ab9ab33d · outbound

This paper cites Choice of PEFT Technique in Continual Learning: Prompt Tuning is Not All You Need.

Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need Choice of PEFT Technique in Continual Learning: Prompt Tuning is Not All You Need

Reference 52

Resolution: verified exact; local_arxiv, observed 2026-08-07T12:43:32.278235Z

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Observation 8350b714-cd9c-48ee-93a0-5438e5ee6d3d · outbound

This paper cites Improving monaural speech enhancement by mapping to fixed simulation space with knowledge distilla- tion.

Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need Improving monaural speech enhancement by mapping to fixed simulation space with knowledge distilla- tion

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:34.697955Z

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Observation 5a855162-6d6b-47ab-a777-886be2a0456f · outbound

This paper cites An ex- perimental study on speech enhancement based on deep neu- ral networks.

Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need An ex- perimental study on speech enhancement based on deep neu- ral networks

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:34.409772Z

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Observation 91310fa3-b7fc-47a0-ab7e-fe3ecee8658a · outbound

This paper cites One-shot replay: Boosting incremental object detection via retrospecting one object.

Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need One-shot replay: Boosting incremental object detection via retrospecting one object

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:34.098139Z

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Observation 6bbee35b-7153-4c03-872c-8d366a3a6ba4 · outbound

This paper cites Learning noise adapters for incremental speech en- hancement.

Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need Learning noise adapters for incremental speech en- hancement

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:33.928619Z

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Observation 8e224428-a021-4c9f-9f59-e00ef442d998 · outbound

This paper cites Bdd100k: A diverse driving dataset for heterogeneous multitask learning.

Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need Bdd100k: A diverse driving dataset for heterogeneous multitask learning

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-07T12:43:31.908329Z

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Observation 01e7cdf5-a102-4c41-b628-3b3878282958 · outbound

This paper cites Speech enhance- ment using deep learning methods: A review.

Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need Speech enhance- ment using deep learning methods: A review

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:33.749188Z

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Observation cbf57245-aac4-49f0-a1a8-9068d8db68ac · outbound

This paper cites Contin- ual learning through synaptic intelligence.

Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need Contin- ual learning through synaptic intelligence

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-07T12:43:32.024776Z

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Observation d4c57b6e-6c59-429b-8b4f-c74b89120407 · outbound

This paper cites Prototype augmentation and self-supervision for incremental learning.

Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need Prototype augmentation and self-supervision for incremental learning

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:33.458188Z

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Observation cabfeb91-2676-4946-9036-de9446a72b96 · outbound

This paper cites 2, 3, 5, 6 10.

Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need 2, 3, 5, 6 10

Reference 162

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:35.433736Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

Observation 5232f059-4817-442b-a4e3-f290abc5e234 · inbound

Continual Knowledge Consolidation LORA for Domain Incremental Learning cites this paper.

Continual Knowledge Consolidation LORA for Domain Incremental Learning Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-04T09:27:52.611645Z

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Observation 48129be5-c1e6-470e-9a56-36cdfcdbff08 · inbound

Few-Shot Domain Incremental Learning via Continual Vision-Language Consolidation cites this paper.

Few-Shot Domain Incremental Learning via Continual Vision-Language Consolidation Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need

Reference 54

Resolution: verified exact; arxiv_id, observed 2026-06-30T06:34:19.419535Z

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