Paper Citation Record · LEDGER

Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts

As of 7 August 2026, this Paper Citation Record lists 66 of 66 outbound references and 3 inbound Pith citation observations for arXiv:2507.07100.

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

pith.paper-citation-record.v1
2507.07100 v1

Coverage vector

measured 66 of 66 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T18:52:23.313731Z

measured 69 of 69 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T02:44:17.805274Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T19:43:54.821561Z

Reference resolution

66 of 66 outbound references displayed

verified exact3
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b27d22b8-a9c3-4666-8f5e-2d19900cbdf3 · outbound

This paper cites an unresolved cited work.

Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts Unresolved cited work

Reference 1

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Observation 69c8fc98-95be-4b16-b937-33aab54f1fa7 · outbound

This paper cites Learning imbalanced datasets with label-distribution-aware margin loss.

Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts Learning imbalanced datasets with label-distribution-aware margin loss

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:52:24.418832Z

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Observation 11455607-1cd1-4e00-952d-706b9d3905e2 · outbound

This paper cites V., Bowyer, K.

Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts V., Bowyer, K

Reference 3

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Observation 1bf744d5-55da-45cb-aa04-8d1d4033a1f4 · outbound

This paper cites Adaptformer: Adapting vision transformers for scalable visual recognition.

Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts Adaptformer: Adapting vision transformers for scalable visual recognition

Reference 4

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Observation e2afb237-a4fa-4a20-bb91-02fa27b31d41 · outbound

This paper cites C., and Hero, A.

Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts C., and Hero, A

Reference 5

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Observation 36dc9025-f48c-4780-9249-d267b8b555f3 · outbound

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Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts Unresolved cited work

Reference 6

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Observation 37adbbf4-d5eb-4f94-8b53-9194ae380418 · outbound

This paper cites A continual learning survey: Defying forgetting in classification tasks.

Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts A continual learning survey: Defying forgetting in classification tasks

Reference 7

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

Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts Imagenet: A large-scale hierarchical image database

Reference 8

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This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts An image is worth 16x16 words: Transformers for image recognition at scale

Reference 9

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Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts and Chiriatti, M

Reference 10

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Observation ca4e1aaa-046a-4af4-8e8f-d8fcf2229bdd · outbound

This paper cites A survey on concept drift adaptation.

Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts A survey on concept drift adaptation

Reference 11

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This paper cites Pre-trained models: Past, present and future.

Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts Pre-trained models: Past, present and future

Reference 12

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This paper cites A., and Li, S.

Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts A., and Li, S

Reference 13

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This paper cites Deep residual learning for image recognition.

Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts Deep residual learning for image recognition

Reference 14

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Observation da1e816b-202f-429b-957e-086a1ac87eb3 · outbound

This paper cites J., Hariharan, B., and Lim, S.

Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts J., Hariharan, B., and Lim, S

Reference 15

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Observation a2fef37d-d7b3-400a-b7a5-d78db1003846 · outbound

This paper cites Decoupling representation and classifier for long-tailed recognition.

Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts Decoupling representation and classifier for long-tailed recognition

Reference 16

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Observation c54db010-6b6b-4fce-8298-56ac6072ec5f · outbound

This paper cites Learnability and algorithm for continual learning.

Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts Learnability and algorithm for continual learning

Reference 17

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Observation 8c46a7ee-f8ff-4d46-a879-a25b414ac56f · outbound

This paper cites P., Wang, Y., Shahbazi, M., Hong, X., and Van Gool, L.

Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts P., Wang, Y., Shahbazi, M., Hong, X., and Van Gool, L

Reference 18

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Observation ed048c1c-79da-4235-bd24-6ab5c2754e86 · outbound

This paper cites Enhancing class-imbalanced learning with pre-trained guidance through class-conditional knowledge distillation.

Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts Enhancing class-imbalanced learning with pre-trained guidance through class-conditional knowledge distillation

Reference 19

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Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts S., Indyk, P., and Katabi, D

Reference 20

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This paper cites Scaling & shifting your features: A new baseline for efficient model tuning.

Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts Scaling & shifting your features: A new baseline for efficient model tuning

Reference 21

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Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts Unresolved cited work

Reference 22

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Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts and Maltoni, D

Reference 23

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Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts D., and van de Weijer, J

Reference 24

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Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts D., Gong, D., Parveneh, A., Abbasnejad, E., and Hengel, A

Reference 25

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Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts Long-tail learning via logit adjustment

Reference 26

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Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts A novel neighborhood-weighted sampling method for imbalanced datasets

Reference 27

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Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts Pytorch: An imperative style, high-performance deep learning library

Reference 28

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Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts Moment matching for multi-source domain adaptation

Reference 29

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Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts Adaptive adapter routing for long-tailed class-incremental learning

Reference 30

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Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts Unresolved cited work

Reference 31

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Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts Balanced meta-softmax for long-tailed visual recognition

Reference 32

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Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts Fscil-eaca: Few-shot class-incremental learning network based on embedding augmentation and classifier adaptation for image classification

Reference 33

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Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts Imagenet large scale visual recognition challenge

Reference 34

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Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts and Wang, H

Reference 35

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Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts Meta-weight-net: Learning an explicit mapping for sample weighting

Reference 36

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Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts S., Karlinsky, L., Gutta, V., Cascante-Bonilla, P., Kim, D., Arbelle, A., Panda, R., Feris, R., and Kira, Z

Reference 37

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Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts Pilot: A pre-trained model-based continual learning toolbox, 2025

Reference 38

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Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts M., Tuytelaars, T., and Tolias, A

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:52:23.862024Z

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Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts Deep hashing network for unsupervised domain adaptation

Reference 40

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Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts A comprehensive survey of continual learning: Theory, method and application

Reference 41

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Observation 49f680cb-a6c6-4332-b8c8-a79b9b2c555b · outbound

This paper cites Long-tailed Recognition by Routing Diverse Distribution-Aware Experts.

Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts Long-tailed Recognition by Routing Diverse Distribution-Aware Experts

Reference 42

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

Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts S-prompts learning with pre-trained transformers: An occam’s razor for domain incremental learning

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:52:23.814690Z

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Observation 274ab7a2-bb82-4f04-92b9-c199c264c0cc · outbound

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

Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts Dualprompt: Complementary prompting for rehearsal-free continual learning

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:52:23.799156Z

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Observation 917fcb4c-a40a-440c-8c3b-93d30eebeaf4 · outbound

This paper cites Learning to prompt for continual learning.

Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts Learning to prompt for continual learning

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:52:23.782529Z

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Observation 53f53871-eef5-49e3-bd39-56b6833a98f5 · outbound

This paper cites Herding dynamical weights to learn.

Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts Herding dynamical weights to learn

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:52:23.763676Z

Source-reported events for the cited work

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Observation a4e5e62b-f201-4ace-9211-e716112f7237 · outbound

This paper cites Multi-view correlation distillation for incremental object detection.

Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts Multi-view correlation distillation for incremental object detection

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:52:23.748653Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e9cd83ca-f9fd-4322-91e2-493a8acc71f7 · outbound

This paper cites and Xu, Z.

Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts and Xu, Z

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:52:23.733491Z

Source-reported events for the cited work

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

This paper cites Identifying and Compensating for Feature Deviation in Imbalanced Deep Learning.

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

Reference 49

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Observation b80ce391-e22e-4e79-a5b7-ff68edaf2ab5 · outbound

This paper cites Contextualizing meta-learning via learning to decompose.

Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts Contextualizing meta-learning via learning to decompose

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:52:23.718457Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9405477b-7e44-42e4-97c8-fd47f202f5f3 · outbound

This paper cites Boosting continual learning of vision-language models via mixture-of-experts adapters.

Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts Boosting continual learning of vision-language models via mixture-of-experts adapters

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:52:23.703706Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0db91da6-5bd4-4f25-b2e2-b7a5c9920672 · outbound

This paper cites Slca: Slow learner with classifier alignment for continual learning on a pre-trained model.

Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts Slca: Slow learner with classifier alignment for continual learning on a pre-trained model

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:52:23.687877Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 52b542fa-1cf9-4f8c-926d-272993b612f6 · outbound

This paper cites Range loss for deep face recognition with long-tailed training data.

Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts Range loss for deep face recognition with long-tailed training data

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:52:23.672005Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3a129fcc-2967-4667-beaa-4eaa8183ed2c · outbound

This paper cites Self-Supervised Aggregation of Diverse Experts for Test-Agnostic Long-Tailed Recognition.

Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts Self-Supervised Aggregation of Diverse Experts for Test-Agnostic Long-Tailed Recognition

Reference 54

Resolution: verified exact; local_arxiv, observed 2026-08-06T18:52:23.443135Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4e8e551d-9247-41aa-afea-8fc09b3762e6 · outbound

This paper cites Deep long-tailed learning: A survey.

Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts Deep long-tailed learning: A survey

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:52:23.655749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 289e34bf-5e70-43a7-8629-84dfa1791b09 · outbound

This paper cites Mgsvf: Multi-grained slow versus fast framework for few-shot class-incremental learning.

Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts Mgsvf: Multi-grained slow versus fast framework for few-shot class-incremental learning

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:52:23.639079Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9dfa635e-de0d-4048-9b11-3b76d62cac40 · outbound

This paper cites Multi-layer rehearsal feature augmentation for class-incremental learning.

Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts Multi-layer rehearsal feature augmentation for class-incremental learning

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:52:23.622726Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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

This paper cites Class-Incremental Learning: A Survey.

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

Reference 58

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Source-reported events for the cited work

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Observation d2052e85-73a3-4734-826c-5b5b2b62a115 · outbound

This paper cites A model or 603 exemplars: Towards memory-efficient class-incremental learning.

Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts A model or 603 exemplars: Towards memory-efficient class-incremental learning

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:52:23.606577Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 90953e68-43fe-4a26-9785-06f9575d5651 · outbound

This paper cites Revisiting class-incremental learning with pre-trained models: Generalizability and adaptivity are all you need.

Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts Revisiting class-incremental learning with pre-trained models: Generalizability and adaptivity are all you need

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:52:23.588141Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 37f1ec60-f866-4122-83f0-a5cf23547fe8 · outbound

This paper cites Dual Consolidation for Pre-Trained Model-Based Domain-Incremental Learning.

Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts Dual Consolidation for Pre-Trained Model-Based Domain-Incremental Learning

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-06T18:52:23.289960Z

Source-reported events for the cited work

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Observation eafa8950-b649-437a-baa3-6613c7f4ae1f · outbound

This paper cites Expandable subspace ensemble for pre-trained model-based class-incremental learning.

Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts Expandable subspace ensemble for pre-trained model-based class-incremental learning

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:52:23.571089Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6682a047-d8cd-49f3-9324-5dcf44580b67 · outbound

This paper cites Acil: Analytic class-incremental learning with absolute memorization and privacy protection.

Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts Acil: Analytic class-incremental learning with absolute memorization and privacy protection

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:52:23.553667Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 21f9e44f-ae05-4b25-8ac7-de0038a05d36 · outbound

This paper cites Gkeal: Gaussian kernel embedded analytic learning for few-shot class incremental task.

Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts Gkeal: Gaussian kernel embedded analytic learning for few-shot class incremental task

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:52:23.536622Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c8f7bdf0-7e95-4b96-a3ee-59cfd2ffbe6c · outbound

This paper cites Ds-al: A dual-stream analytic learning for exemplar-free class-incremental learning.

Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts Ds-al: A dual-stream analytic learning for exemplar-free class-incremental learning

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:52:23.520193Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation da772543-ad64-4113-80cf-86dd66facd33 · outbound

This paper cites write newline.

Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts write newline

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-06T18:52:23.313731Z

Source-reported events for the cited work

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Pith citing papers

Observation 855007c4-bb32-48a7-bd67-8e7ea523849e · inbound

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning cites this paper.

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-14T19:12:50.580789Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation beffce55-9cb0-4775-b537-5788cbd37451 · inbound

Energy-Structured Low-Rank Adaptation for Continual Learning cites this paper.

Energy-Structured Low-Rank Adaptation for Continual Learning Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts

Reference 2

Resolution: metadata mismatch; arxiv_id, observed 2026-06-29T19:43:54.823416Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation dd1418d8-52df-4d21-830a-4b065768e5f7 · inbound

MIDiff: Tackling Sparsity and Imbalance in Mobile Usage Generation via Multivariate-Imaging Diffusion cites this paper.

MIDiff: Tackling Sparsity and Imbalance in Mobile Usage Generation via Multivariate-Imaging Diffusion Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-02T02:44:17.805274Z

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