Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning | Paper Citation Record

Observation 09e95b50-7d37-42cb-a936-cf686c8a6e46 · outbound

This paper cites Deep learning.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning Deep learning

Reference 1

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This paper cites Deep learning for visual understanding: A review.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning Deep learning for visual understanding: A review

Reference 2

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Observation cd2a61ab-2391-40fa-89a1-b4b8dc0230b8 · outbound

This paper cites Class-incremental learning via deep model consolidation.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning Class-incremental learning via deep model consolidation

Reference 3

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Observation 8d00cd79-fdab-4bc4-905e-94eb4bffa40b · outbound

This paper cites Class-incremental learning: A survey.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning Class-incremental learning: A survey

Reference 4

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Observation 596a9c43-34fd-4858-a57b-ce0e4ed3c350 · outbound

This paper cites Overcoming catastrophic forgetting in neural networks.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning Overcoming catastrophic forgetting in neural networks

Reference 5

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Observation 6dbf4750-05e1-4f26-afbb-17227e03b179 · outbound

This paper cites Taking a closer look at domain shift: Category-level adversaries for semantics consistent domain adaptation.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning Taking a closer look at domain shift: Category-level adversaries for semantics consistent domain adaptation

Reference 6

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Observation d90da55e-d772-4766-b00a-bcd4704d613f · outbound

This paper cites Generalizing to unseen domains: A survey on domain generalization.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning Generalizing to unseen domains: A survey on domain generalization

Reference 7

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Observation af0f906a-ba18-4b01-a11d-9e1b60bd37da · outbound

This paper cites Class-incremental learning: Survey and performance evaluation on image classification.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning Class-incremental learning: Survey and performance evaluation on image classification

Reference 8

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Observation c44afa4a-6d1a-4d67-9919-e9a4171bcbf7 · outbound

This paper cites Reinforced continual learning.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning Reinforced continual learning

Reference 9

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Observation 08de2e90-cbe3-4d3b-872a-a307e51d7bef · outbound

This paper cites Class-incremental learning via dual augmentation.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning Class-incremental learning via dual augmentation

Reference 10

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Observation fae061f6-6c8b-4a28-a4a0-9036e3b06783 · outbound

This paper cites Mimicking the oracle: An initial phase decorrelation approach for class incremental learning.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning Mimicking the oracle: An initial phase decorrelation approach for class incremental learning

Reference 11

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Observation b0f55771-34a9-4ff3-bde6-2443186c92ba · outbound

This paper cites Deep continual learn- ing for emerging emotion recognition.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning Deep continual learn- ing for emerging emotion recognition

Reference 12

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Observation d00018c5-cce8-4a9a-904d-0c59430e828b · outbound

This paper cites Cross-modal alternating learning with task-aware representations for continual learning.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning Cross-modal alternating learning with task-aware representations for continual learning

Reference 13

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Observation e2778ac1-d942-48c3-8b69-d3998a83ed12 · outbound

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

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning Dualprompt: Complementary prompting for rehearsal-free continual learning

Reference 14

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Observation bd6978d2-bb5e-48d4-8a2e-45aef3eb730d · outbound

This paper cites Domain generalization with adversarial feature learning.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning Domain generalization with adversarial feature learning

Reference 15

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Observation a5abd36f-c524-4c8e-8281-b145a1b5bd91 · outbound

This paper cites FSDR: frequency space domain randomization for domain generalization.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning FSDR: frequency space domain randomization for domain generalization

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T06:08:24.703679Z

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Observation 766ff559-4452-40b5-a687-3ea19e64be92 · outbound

This paper cites Manydg: Many-domain general- ization for healthcare applications.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning Manydg: Many-domain general- ization for healthcare applications

Reference 17

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Observation 4f969a8f-5508-43cf-a168-199089a2552b · outbound

This paper cites Learning to generalize: Meta-learning for domain generalization.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning Learning to generalize: Meta-learning for domain generalization

Reference 18

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Observation 781acf49-6fcc-4876-ad56-355eb0002e22 · outbound

This paper cites Style normalization and resti- tution for domain generalization and adaptation.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning Style normalization and resti- tution for domain generalization and adaptation

Reference 19

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Observation 597122b2-cae4-436a-9e95-5ce39c8285e9 · outbound

This paper cites Generalizing to unseen domains: A survey on domain generalization.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning Generalizing to unseen domains: A survey on domain generalization

Reference 20

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Observation ff51d1ff-3a02-49fd-ae1c-77fc51d747ea · outbound

This paper cites Knowledge distillation-based domain-invariant representation learning for domain generalization.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning Knowledge distillation-based domain-invariant representation learning for domain generalization

Reference 21

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Observation d08c28b6-65b1-4fa6-a2d0-391914723a48 · outbound

This paper cites Using noise to compute error surfaces in connectionist networks: A novel means of reducing catastrophic forgetting.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning Using noise to compute error surfaces in connectionist networks: A novel means of reducing catastrophic forgetting

Reference 22

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Observation 941d25f8-2107-411d-a660-51e97fe9bff8 · outbound

This paper cites Effect of scale on catastrophic forgetting in neural networks.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning Effect of scale on catastrophic forgetting in neural networks

Reference 23

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Observation c8b316e8-ff46-4858-b130-afd92778c16c · outbound

This paper cites Class incremental learning with multi-teacher distillation.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning Class incremental learning with multi-teacher distillation

Reference 24

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Observation b3f722ba-853d-4de3-8bf8-de08c4f45c20 · outbound

This paper cites What matters in graph class incremental learning? an information preservation perspective.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning What matters in graph class incremental learning? an information preservation perspective

Reference 25

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Observation e1abfbe1-3e3d-4fa4-aa0d-8ae9943975d8 · outbound

This paper cites Multi- label continual learning using augmented graph convolutional network.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning Multi- label continual learning using augmented graph convolutional network

Reference 26

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Observation 51ade523-44db-469a-a744-b12774412961 · outbound

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

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning Fetril: Feature translation for exemplar-free class-incremental learning

Reference 27

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Observation b8819bfc-2db1-4dc7-a554-9ee8ec2bcea8 · outbound

This paper cites Ordisco: Effective and efficient usage of incremental unlabeled data for semi-supervised continual learning.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning Ordisco: Effective and efficient usage of incremental unlabeled data for semi-supervised continual learning

Reference 28

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Observation 0056fd2c-2b70-4ae6-b6e1-c1d800163966 · outbound

This paper cites DDGR: continual learning with deep diffusion- based generative replay.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning DDGR: continual learning with deep diffusion- based generative replay

Reference 29

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Observation 29618133-8576-4fe7-82b9-7839452e1505 · outbound

This paper cites IB-DRR - incremental learning with information-back discrete representation replay.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning IB-DRR - incremental learning with information-back discrete representation replay

Reference 30

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Observation 1f008e3c-b75b-4449-bdbc-7c7b63260797 · outbound

This paper cites Learning to learn without forgetting by maximizing transfer and minimizing interference.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning Learning to learn without forgetting by maximizing transfer and minimizing interference

Reference 31

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Observation 7cc4b5f0-9bc2-4524-9d62-fba79c528b95 · outbound

This paper cites Gradient based sample selection for online continual learning.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning Gradient based sample selection for online continual learning

Reference 32

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Observation 0db1e033-2e00-4e39-a74f-740eb656f281 · outbound

This paper cites Continual attentive fusion for incremental learning in semantic segmentation.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning Continual attentive fusion for incremental learning in semantic segmentation

Reference 33

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Observation 46d7e86a-70a0-47e4-b28a-b5340b6467bb · outbound

This paper cites Lifelong learning with dynamically expandable networks.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning Lifelong learning with dynamically expandable networks

Reference 34

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Observation e7234d00-0afe-4257-aaf1-49d446d6495a · outbound

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

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning Coda-prompt: Contin- ual decomposed attention-based prompting for rehearsal-free continual learning

Reference 35

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Observation 941229a7-850b-4d9b-bbee-0ffbe7fa3280 · outbound

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

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning A model or 603 exemplars: Towards memory-efficient class-incremental learning

Reference 36

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Observation 99c63b65-5f80-4626-b67f-0703050d20ec · outbound

This paper cites Unified adaptive relevance distinguishable attention network for image-text matching.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning Unified adaptive relevance distinguishable attention network for image-text matching

Reference 37

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Observation f3e404b4-0a94-478b-8caf-0b5746ecb737 · outbound

This paper cites Adaptive aggregation networks for class-incremental learning.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning Adaptive aggregation networks for class-incremental learning

Reference 38

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Observation 84e155d3-8915-4e24-afe2-5db8e5d62add · outbound

This paper cites Learning to prompt for continual learning.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning Learning to prompt for continual learning

Reference 39

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Observation 83b6ff7e-9ae2-4854-8f1b-6f64f3a6f88c · outbound

This paper cites Large scale incremental learning.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning Large scale incremental learning

Reference 40

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Observation 3ab748bb-2b50-4f07-a077-2e099296c4ab · outbound

This paper cites IL2M: class incremental learning with dual memory.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning IL2M: class incremental learning with dual memory

Reference 41

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Observation f29d714b-c692-41fb-be3c-bd1ce88609bc · outbound

This paper cites Cmoa: Contrastive mixture of adapters for generalized few-shot continual learning.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning Cmoa: Contrastive mixture of adapters for generalized few-shot continual learning

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T06:08:24.648386Z

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Observation 305d070f-9a03-4c3f-b380-10388c034034 · outbound

This paper cites Theory on forgetting and generalization of continual learning.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning Theory on forgetting and generalization of continual learning

Reference 43

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Observation 4cecb358-36a0-4934-844d-abdf9df64efe · outbound

This paper cites Formalizing the generalization- forgetting trade-off in continual learning.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning Formalizing the generalization- forgetting trade-off in continual learning

Reference 44

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Observation dcbe8cb9-d655-4042-82a6-28ed71608762 · outbound

This paper cites Probabilistic group mask guided discrete opti- mization for incremental learning.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning Probabilistic group mask guided discrete opti- mization for incremental learning

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T06:08:24.914332Z

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Observation 6bb2e580-5ef8-40db-bc0a-e7d49f4742f4 · outbound

This paper cites Domain general- ization: A survey.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning Domain general- ization: A survey

Reference 46

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Observation d42e36d3-a3c9-4821-ac5b-fa2c80c5b8c3 · outbound

This paper cites A dual-augmentor framework for domain generalization in 3d human pose.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning A dual-augmentor framework for domain generalization in 3d human pose

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T06:08:24.853097Z

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Observation 0e148700-d369-447e-a2f1-df086be328ca · outbound

This paper cites MADG: margin-based adversarial learning for domain generalization.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning MADG: margin-based adversarial learning for domain generalization

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T06:08:24.719805Z

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Observation f7751eae-752a-4e79-b70c-2e61c6b04e41 · outbound

This paper cites Domain generalization via encoding and resampling in a unified latent space.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning Domain generalization via encoding and resampling in a unified latent space

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T06:08:24.926851Z

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Observation a05f7ee4-fa4c-4453-95f4-43b152e934fe · outbound

This paper cites Learning generalized knowledge from a single domain on urban-scene segmentation.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning Learning generalized knowledge from a single domain on urban-scene segmentation

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T06:08:24.692568Z

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Observation 58de0dea-98b2-4489-8838-b4f881764fb0 · outbound

This paper cites Out-of-distribution generalization with causal invariant transformations.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning Out-of-distribution generalization with causal invariant transformations

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T06:08:24.664619Z

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

Observation 1a438ed7-9a9f-4825-944a-10ecdf375a7d · outbound

This paper cites Causality inspired representation learning for domain generalization.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning Causality inspired representation learning for domain generalization

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T06:08:24.872733Z

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 875eabdc-a80a-4996-933d-4b667dbeb777 · outbound

This paper cites Distance-based hyperspherical classification for multi-source open-set.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning Distance-based hyperspherical classification for multi-source open-set

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T06:08:24.661232Z

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 febfe05b-8434-454c-a19b-afa6694f0ce3 · outbound

This paper cites Adaptive Risk Minimization: Learning to Adapt to Domain Shift.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning Adaptive Risk Minimization: Learning to Adapt to Domain Shift

Reference 54

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T01:46:13.939401Z

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 0a9028a9-5fb3-4f60-ae60-3283ef06116b · outbound

This paper cites Learning intrinsic invariance within intra-class for domain generalization.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning Learning intrinsic invariance within intra-class for domain generalization

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T06:08:24.678782Z

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 e7c25679-387c-4701-8b91-8a423f46a3da · outbound

This paper cites Domain-Unified Prompt Representations for Source-Free Domain Generalization.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning Domain-Unified Prompt Representations for Source-Free Domain Generalization

Reference 56

Resolution: verified exact; arxiv_id, observed 2026-05-12T01:46:13.932814Z

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 ed8b9a91-54bf-4a7e-a5ba-5f1c341b8720 · outbound

This paper cites Promptstyler: Prompt- driven style generation for source-free domain generalization.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning Promptstyler: Prompt- driven style generation for source-free domain generalization

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T06:08:24.675305Z

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 462d5551-fa7d-4d8d-8d16-cf393b7bc8ec · outbound

This paper cites Dpstyler: Dynamic promptstyler for source-free domain generalization.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning Dpstyler: Dynamic promptstyler for source-free domain generalization

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T06:08:24.865767Z

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 e418c8a4-e1b2-48b8-9770-5cc9fa6d5e48 · outbound

This paper cites HCVP: leveraging hierarchical contrastive visual prompt for domain generalization.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning HCVP: leveraging hierarchical contrastive visual prompt for domain generalization

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T06:08:24.896488Z

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 d8c889d7-568f-4a0d-a96f-52d4c7b29864 · outbound

This paper cites Domain generalization using shape representation.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning Domain generalization using shape representation

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T06:08:24.946250Z

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 d5dfbf0c-6168-415c-9dfb-d3a99e6b77da · outbound

This paper cites Progressive diversity generation for single domain generalization.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning Progressive diversity generation for single domain generalization

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T06:08:24.682001Z

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 27af0643-8511-4179-9b72-229c4b259494 · outbound

This paper cites Adversarial teacher- student representation learning for domain generalization.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning Adversarial teacher- student representation learning for domain generalization

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T06:08:24.700047Z

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 1d79436e-a56a-42d0-8813-177867cfa599 · outbound

This paper cites Federated adversarial domain hallucination for privacy-preserving domain generalization.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning Federated adversarial domain hallucination for privacy-preserving domain generalization

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T06:08:24.658372Z

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 62d3bf10-ebb6-4062-b2e9-b014804c8ee1 · outbound

This paper cites Domain generalization with mixstyle.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning Domain generalization with mixstyle

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T06:08:24.879747Z

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 456de519-e27e-4326-a727-95a0da669008 · outbound

This paper cites On generalizing beyond domains in cross-domain continual learning.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning On generalizing beyond domains in cross-domain continual learning

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T06:08:24.883224Z

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 fb5cca52-90fd-402d-87b6-db1f971b8264 · outbound

This paper cites Mul- tivariate prototype representation for domain-generalized incremental learning.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning Mul- tivariate prototype representation for domain-generalized incremental learning

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T06:08:24.886811Z

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 52ac2382-5e2e-43f7-aee2-3724926b9c55 · outbound

This paper cites icarl: Incre- mental classifier and representation learning.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning icarl: Incre- mental classifier and representation learning

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T06:08:24.929836Z

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 7b66a438-7d6c-412f-92da-4ecf631e0730 · outbound

This paper cites DER: dynamically expandable representation for class incremental learning.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning DER: dynamically expandable representation for class incremental learning

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T06:08:24.726673Z

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 a57673a5-8426-4aad-a4d9-10576cce8b66 · outbound

This paper cites Podnet: Pooled outputs distillation for small-tasks incremental learning.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning Podnet: Pooled outputs distillation for small-tasks incremental learning

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T06:08:24.733826Z

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 736e51eb-5ab1-4ae7-8639-0fdbd892f256 · outbound

This paper cites Maximum-entropy adver- sarial data augmentation for improved generalization and robustness.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning Maximum-entropy adver- sarial data augmentation for improved generalization and robustness

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T06:08:24.685471Z

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 6278db46-356b-4c61-9e4f-f1b520142bd4 · outbound

This paper cites Uncertainty-guided model generalization to unseen domains.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning Uncertainty-guided model generalization to unseen domains

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T06:08:24.753729Z

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 c7c24a1a-ce1a-4b1d-86c8-193ed2e49e07 · outbound

This paper cites Grad-cam: Visual explanations from deep networks via gradient-based localization.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning Grad-cam: Visual explanations from deep networks via gradient-based localization

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T06:08:24.746986Z

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 6461d523-041a-433d-908c-00b29b7ccf27 · outbound

This paper cites mixup: Beyond Empirical Risk Minimization.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning mixup: Beyond Empirical Risk Minimization

Reference 73

Resolution: verified exact; arxiv_id, observed 2026-05-12T15:34:18.648384Z

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 a3a8105b-cf16-4c8d-8eee-4dafcd3b86ce · outbound

This paper cites Cutmix: Reg- ularization strategy to train strong classifiers with localizable features.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning Cutmix: Reg- ularization strategy to train strong classifiers with localizable features

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T06:08:24.671791Z

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 d0158d1e-03c3-4dca-a364-e8c4d2bafc2b · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning Distilling the Knowledge in a Neural Network

Reference 75

Resolution: verified exact; local_arxiv, observed 2026-05-12T01:46:13.935830Z

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 68c7af1f-907f-4fc6-8ba2-37e17d6df0f4 · outbound

This paper cites Domain generalization through meta- learning: a survey.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning Domain generalization through meta- learning: a survey

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T06:08:24.668099Z

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 6bbec07c-73ed-49cb-9621-84aa40bb2771 · outbound

This paper cites Deep hashing network for unsupervised domain adaptation.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning Deep hashing network for unsupervised domain adaptation

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T06:08:24.893262Z

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 6843e9fb-5baf-4fda-b3f5-a7a42992b03b · outbound

This paper cites Semi- supervised domain adaptation via minimax entropy.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning Semi- supervised domain adaptation via minimax entropy

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T06:08:24.938835Z

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 d4b16c21-d20d-4bab-934e-4f41a2d91055 · outbound

This paper cites Deeper, broader and artier domain generalization.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning Deeper, broader and artier domain generalization

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T06:08:24.862069Z

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 e96afb9b-af8c-494c-844d-0d1ae0772dd4 · outbound

This paper cites Pytorch: An imperative style, high- performance deep learning library.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning Pytorch: An imperative style, high- performance deep learning library

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T06:08:24.766339Z

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 647fcb18-c1a3-4451-99bf-4250f3275527 · outbound

This paper cites Deep residual learning for image recognition.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning Deep residual learning for image recognition

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T06:08:24.772951Z

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 21ff7c17-103b-4870-8f6b-08ad1d56ebf3 · outbound

This paper cites FOSTER: feature boosting and compression for class-incremental learning.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning FOSTER: feature boosting and compression for class-incremental learning

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T06:08:24.776681Z

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 df75d28a-5bb8-4365-b1a4-e92e626d59db · outbound

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

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning Ds- al: A dual-stream analytic learning for exemplar-free class-incremental learning

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T06:08:24.780333Z

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 7eb5050f-e023-4dc8-a9d6-aaa20e5477a3 · outbound

This paper cites Task-agnostic guided feature expansion for class-incremental learning.

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning Task-agnostic guided feature expansion for class-incremental learning

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T06:08:24.769338Z

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.

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