BPG: Balancing Plasticity and Generalization for Domain Incremental Learning | Paper Citation Record

Observation 93c8902d-c1bd-4101-bcad-21ee304f56d9 · outbound

This paper cites Query- aware cross-mixup and cross-reconstruction for few-shot fine-grained image classification,.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Query- aware cross-mixup and cross-reconstruction for few-shot fine-grained image classification,

Reference 1

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Observation 3454306f-498a-413f-8964-209cd7891c0c · outbound

This paper cites Dynamic Integration of Task-Specific Adapters for Class Incremental Learning.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Dynamic Integration of Task-Specific Adapters for Class Incremental Learning

Reference 2

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Observation e109f651-a9b7-481d-a61d-ea2b985638b3 · outbound

This paper cites Class-independent increment: An efficient approach for multi-label class-incremental learning,.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Class-independent increment: An efficient approach for multi-label class-incremental learning,

Reference 3

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Observation 6da6ce5d-ec61-42e8-8306-cd11ef82ce36 · outbound

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

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Learn by reasoning: Analogical weight generation for few-shot class- incremental learning,

Reference 4

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Observation fd73422d-8b2b-4e0f-8aaa-1b2d66f1f3ef · outbound

This paper cites Gfpl: Generative federated prototype learning for resource-constrained and data-imbalanced vision task,.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Gfpl: Generative federated prototype learning for resource-constrained and data-imbalanced vision task,

Reference 5

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Observation 6769d752-01fe-4db3-af20-c8a5886ec095 · outbound

This paper cites A robust moving object detection in multi-scenario big data for video surveillance,.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning A robust moving object detection in multi-scenario big data for video surveillance,

Reference 6

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Observation 478e5a4b-97c6-4106-9eff-bc3f0a12feeb · outbound

This paper cites Learning endogenous attention for incremental object detection,.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Learning endogenous attention for incremental object detection,

Reference 7

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Observation 8a7360cf-7359-48dc-9425-9416f7d533de · outbound

This paper cites Shared & domain self-adaptive experts with frequency-aware discrim- ination for continual test-time adaptation,.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Shared & domain self-adaptive experts with frequency-aware discrim- ination for continual test-time adaptation,

Reference 8

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Observation 1dd56b6d-2e63-48fd-a5a7-746a43f980c5 · outbound

This paper cites Decenter: Density-center guided perception enhancement for uav object detection,.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Decenter: Density-center guided perception enhancement for uav object detection,

Reference 9

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Observation bd1c3178-f843-4c92-9807-48b1a44000dd · outbound

This paper cites Towards open-vocabulary video semantic segmentation,.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Towards open-vocabulary video semantic segmentation,

Reference 10

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Observation c0a2b091-3f22-4454-90de-d0cac6d74343 · outbound

This paper cites Holistic prototype attention network for few-shot video object segmentation,.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Holistic prototype attention network for few-shot video object segmentation,

Reference 11

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Observation 7e471be7-577e-4f08-9ffa-ad11f9431bed · outbound

This paper cites Catastrophic interference in connec- tionist networks: The sequential learning problem,.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Catastrophic interference in connec- tionist networks: The sequential learning problem,

Reference 12

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Observation 7a870278-3247-47ca-a478-81dbc781d385 · outbound

This paper cites LPT: Less-overfitting Prompt Tuning for Vision-Language Model.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning LPT: Less-overfitting Prompt Tuning for Vision-Language Model

Reference 13

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Observation c34423b9-3a34-47c6-9993-162b4eeba1ea · outbound

This paper cites Goal: Geometrically optimal alignment for continual generalized cat- egory discovery,.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Goal: Geometrically optimal alignment for continual generalized cat- egory discovery,

Reference 14

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Observation df940997-6dae-4a54-a3a4-4e37eb81b406 · outbound

This paper cites Trajectory-diversity-driven robust vision-and-language navigation,.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Trajectory-diversity-driven robust vision-and-language navigation,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-12T17:12:13.866876Z

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Observation d0bd104f-5d54-4249-8890-28ba9b85d050 · outbound

This paper cites Consistent supervised-unsupervised alignment for general- ized category discovery,.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Consistent supervised-unsupervised alignment for general- ized category discovery,

Reference 16

Resolution: verified exact; raw_fallback, observed 2026-08-12T17:12:15.043569Z

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Observation 5fb5ebfa-565d-4482-b39c-27629f27339b · outbound

This paper cites Beyond CLIP Generalization: Against Forward&Backward Forgetting Adapter for Continual Learning of Vision-Language Models.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Beyond CLIP Generalization: Against Forward&Backward Forgetting Adapter for Continual Learning of Vision-Language Models

Reference 17

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Observation 4e4e8bfd-47b9-4c8c-b71d-4a9791c02a47 · outbound

This paper cites Preventing catastrophic forgetting in continuous online learning for autonomous driving,.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Preventing catastrophic forgetting in continuous online learning for autonomous driving,

Reference 18

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Observation 5dd5c533-aeef-4d0d-af65-9cb5098f4133 · outbound

This paper cites Dynamic memory to alleviate catastrophic forgetting in continual learning with medical imaging,.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Dynamic memory to alleviate catastrophic forgetting in continual learning with medical imaging,

Reference 19

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Observation af57bfce-498b-40d6-a1bf-f75d0635cdba · outbound

This paper cites Continual learning for anomaly detection in surveillance videos,.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Continual learning for anomaly detection in surveillance videos,

Reference 20

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Observation 31a1bf10-b1dc-441e-b17d-d550a6dedff5 · outbound

This paper cites VDC-Agent: When Video Detailed Captioners Evolve Themselves via Agentic Self-Reflection.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning VDC-Agent: When Video Detailed Captioners Evolve Themselves via Agentic Self-Reflection

Reference 21

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Observation b3a76720-3d0b-42c9-aab9-c9f5d741e491 · outbound

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

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning S-prompts learning with pre-trained transformers: An occam’s razor for domain incremental learning,

Reference 22

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Observation 214e65cb-a903-4aaa-b27a-acedd9bd689c · outbound

This paper cites Incremental learning of multi- domain image-to-image translations,.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Incremental learning of multi- domain image-to-image translations,

Reference 23

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Observation 3f2b1d3b-d131-41df-870a-dc74e383cf2f · outbound

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

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Beyond prompt learning: Continual adapter for efficient rehearsal-free continual learn- ing,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-12T17:12:13.919879Z

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Observation 20114435-6c74-4790-a8cd-2087386fbc1a · outbound

This paper cites Video domain incremental learning for human action recognition in home environments,.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Video domain incremental learning for human action recognition in home environments,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-12T17:12:13.925156Z

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Observation d16311bb-d349-4fb8-bdfb-543f10c12081 · outbound

This paper cites Class incremental learning for light-weighted networks,.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Class incremental learning for light-weighted networks,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-12T17:12:13.931134Z

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Observation 0c33a6a2-c84f-4c80-9b1e-e441f06053e4 · outbound

This paper cites Domain incremental object detection based on feature space topology preserving strategy,.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Domain incremental object detection based on feature space topology preserving strategy,

Reference 27

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Observation 2bed86d3-e50d-4973-bda3-03961e8a9ad7 · outbound

This paper cites Inflora: Interference-free low-rank adaptation for continual learning,.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Inflora: Interference-free low-rank adaptation for continual learning,

Reference 28

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Observation 7eafd998-0c99-4d94-acf1-7b6463b65e2d · outbound

This paper cites Dc-lora: Domain correlation low-rank adaptation for domain incremental learning,.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Dc-lora: Domain correlation low-rank adaptation for domain incremental learning,

Reference 29

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Observation a055b1c2-274e-4607-ae1b-641b278c4174 · outbound

This paper cites Selective experience replay for lifelong learn- ing,.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Selective experience replay for lifelong learn- ing,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:12:16.377310Z

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Observation 0c783203-9a95-4122-bad4-b313c1969c01 · outbound

This paper cites Expe- rience replay for continual learning,.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Expe- rience replay for continual learning,

Reference 31

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Observation 5b4bf2b1-14bb-4b03-b723-256a161ce38b · outbound

This paper cites Memory-efficient class- incremental learning for image classification,.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Memory-efficient class- incremental learning for image classification,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:12:16.346515Z

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Observation dc98d747-4f2a-47c4-b750-f613341f7419 · outbound

This paper cites Overcoming catastrophic forgetting in neural networks,.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Overcoming catastrophic forgetting in neural networks,

Reference 33

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Observation 72b7f188-93f8-49ee-8c32-b1b0db0e194b · outbound

This paper cites Continual learning through synaptic intelligence,.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Continual learning through synaptic intelligence,

Reference 34

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Observation 7015fbe4-36af-4343-8384-973a562409a2 · outbound

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

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Memory aware synapses: Learning what (not) to forget,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:12:16.296547Z

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Observation 0d0580ac-eb61-4bb5-8e13-66deb942aa22 · outbound

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

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Subspace Regularizers for Few-Shot Class Incremental Learning

Reference 36

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Observation a36a2ed5-1908-4c95-bfe0-b560b4aaf856 · outbound

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

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Few-shot class-incremental learning via entropy-regularized data-free replay,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:12:16.274972Z

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Observation f9611d18-fb10-4775-aa53-809b0f95d93d · outbound

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

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Multi-granularity knowledge distillation and prototype consistency regularization for class-incremental learning,

Reference 38

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Observation 3697d2cc-9d86-46a6-ae9a-5f0a687734d1 · outbound

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

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Mop-clip: A mixture of prompt-tuned clip models for domain incre- mental learning,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:12:16.237167Z

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Observation 89aba9dc-894b-4e2f-a157-57a25a420be4 · outbound

This paper cites Non- exemplar domain incremental learning via cross-domain concept in- tegration,.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Non- exemplar domain incremental learning via cross-domain concept in- tegration,

Reference 40

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Observation 8ca45079-9d18-4dba-ad77-68920c0be4a9 · outbound

This paper cites Compositional prompting for anti- forgetting in domain incremental learning,.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Compositional prompting for anti- forgetting in domain incremental learning,

Reference 41

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Observation ded497ad-e4fa-43eb-bb63-2d102398a2c1 · outbound

This paper cites Importance- aware shared parameter subspace learning for domain incremental learning,.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Importance- aware shared parameter subspace learning for domain incremental learning,

Reference 42

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Observation d85a63a2-a3d2-4b12-9a49-73cd2f815e94 · outbound

This paper cites Imbalanced continual learning with partitioning reservoir sampling,.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Imbalanced continual learning with partitioning reservoir sampling,

Reference 43

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Observation 7b16e4b9-984a-4788-beab-c75535a8da6c · outbound

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

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Space Rotation with Basis Transformation for Training-free Test-Time Adaptation

Reference 44

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Observation ed4d7e9f-8f67-4ecf-9bb7-35bb5e08e10b · outbound

This paper cites Variational Prototype Replays for Continual Learning.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Variational Prototype Replays for Continual Learning

Reference 45

Resolution: verified exact; local_arxiv, observed 2026-08-12T17:12:14.825256Z

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Observation f20d8a85-e23a-4c16-889d-036b2ee2625d · outbound

This paper cites Continual learning with deep generative replay,.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Continual learning with deep generative replay,

Reference 46

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Observation 985ab14d-b4fc-43bd-87cf-f2d1ca9f66bb · outbound

This paper cites Class-incremental learning with generative classifiers,.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Class-incremental learning with generative classifiers,

Reference 47

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Observation 1386df2d-4dad-41d6-ac00-9751200d634b · outbound

This paper cites Ranpac: Random projections and pre-trained mod- els for continual learning,.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Ranpac: Random projections and pre-trained mod- els for continual learning,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:12:16.114559Z

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Observation 90dcd26c-302a-4ad2-b47d-311eed5dc166 · outbound

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

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Revisiting class-incremental learning with pre-trained models: Generalizability and adaptivity are all you need,

Reference 49

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Observation aed8f657-0ab4-448b-91ba-8a83bcc14a52 · outbound

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

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Slca: Slow learner with classifier alignment for continual learning on a pre-trained model,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:12:16.085074Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f23e44db-66d8-49de-84dd-3279dcfcddf2 · outbound

This paper cites Is parameter isolation better for prompt-based continual learning?.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Is parameter isolation better for prompt-based continual learning?

Reference 52

Resolution: verified exact; raw_fallback, observed 2026-08-12T17:12:14.794599Z

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Observation e1a6a47f-6c2e-4b33-9113-19f673b104f9 · outbound

This paper cites Privacy-preserving continual learning methods for medical image classification: a comparative analysis,.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Privacy-preserving continual learning methods for medical image classification: a comparative analysis,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:12:16.046196Z

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Observation 1d30dce6-6398-4f50-811e-13ab0eca9869 · outbound

This paper cites Preventing zero-shot transfer degradation in continual learning of vision-language models,.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Preventing zero-shot transfer degradation in continual learning of vision-language models,

Reference 54

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Observation 30451b54-179f-4e0d-b170-d739381b6425 · outbound

This paper cites Continual Learning and Catastrophic Forgetting.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Continual Learning and Catastrophic Forgetting

Reference 55

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Observation 7c0d558b-e0cf-4f62-b641-8beda9629daf · outbound

This paper cites Componential prompt-knowledge alignment for domain incremental learning,.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Componential prompt-knowledge alignment for domain incremental learning,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:12:16.014776Z

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Observation b846e05f-8b05-4fae-b143-288dd31c95b2 · outbound

This paper cites A unified continual learning framework with general parameter-efficient tuning,.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning A unified continual learning framework with general parameter-efficient tuning,

Reference 57

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This paper cites Expandable sub- space ensemble for pre-trained model-based class-incremental learn- ing,.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Expandable sub- space ensemble for pre-trained model-based class-incremental learn- ing,

Reference 58

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Observation 8dafebba-af55-4bf7-ad7c-33c0c02f31f3 · outbound

This paper cites Learning multiple visual domains with residual adapters,.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Learning multiple visual domains with residual adapters,

Reference 59

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Observation 5b4fe8e5-fb18-4409-9a69-675c54bb628c · outbound

This paper cites Lora: Low-rank adaptation of large language models.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Lora: Low-rank adaptation of large language models

Reference 60

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This paper cites Parameter-efficient transfer learning for nlp,.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Parameter-efficient transfer learning for nlp,

Reference 61

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This paper cites AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning

Reference 62

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Observation d13ce7bc-8ae7-41f2-9a6e-1ecda9f15567 · outbound

This paper cites DyLoRA: Parameter Efficient Tuning of Pre-trained Models using Dynamic Search-Free Low-Rank Adaptation.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning DyLoRA: Parameter Efficient Tuning of Pre-trained Models using Dynamic Search-Free Low-Rank Adaptation

Reference 63

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Observation c0d8fea4-08d7-4058-8624-5bf63f2dcdab · outbound

This paper cites GeLoRA: Geometric Adaptive Ranks For Efficient LoRA Fine-tuning.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning GeLoRA: Geometric Adaptive Ranks For Efficient LoRA Fine-tuning

Reference 64

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This paper cites Ard-lora: Dynamic rank allocation for parameter-efficient fine-tuning of foundation models with heteroge- neous adaptation needs,.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Ard-lora: Dynamic rank allocation for parameter-efficient fine-tuning of foundation models with heteroge- neous adaptation needs,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:12:15.922523Z

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Observation 3a8961da-16cf-4e67-8598-a39ab52fe49f · outbound

This paper cites Adaptive adapters: An efficient way to incorporate bert into neural machine translation,.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Adaptive adapters: An efficient way to incorporate bert into neural machine translation,

Reference 66

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This paper cites Dynamic adapter meets prompt tuning: Parameter-efficient transfer learning for point cloud analysis,.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Dynamic adapter meets prompt tuning: Parameter-efficient transfer learning for point cloud analysis,

Reference 67

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Observation ea75e945-452b-4a30-a5ca-3ffe62b6f8ee · outbound

This paper cites Adaptive adapter routing for long-tailed class-incremental learning,.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Adaptive adapter routing for long-tailed class-incremental learning,

Reference 68

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Observation a6ae529c-c69c-4c87-a921-3b4ffc709143 · outbound

This paper cites Isolation and impartial aggregation: A paradigm of incremental learning without interference,.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Isolation and impartial aggregation: A paradigm of incremental learning without interference,

Reference 69

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This paper cites Lifelong language pretraining with distribution-specialized experts,.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Lifelong language pretraining with distribution-specialized experts,

Reference 70

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

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Boosting continual learning of vision-language models via mixture-of-experts adapters,

Reference 71

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This paper cites Moe-adapters++: Towards more efficient continual learning of vision-language models via dynamic mixture-of-experts adapters,.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Moe-adapters++: Towards more efficient continual learning of vision-language models via dynamic mixture-of-experts adapters,

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:12:15.800213Z

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

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Imagenet: A large-scale hierarchical image database,

Reference 73

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This paper cites Shalev-Shwartz and S.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Shalev-Shwartz and S

Reference 74

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This paper cites Mohri, A.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Mohri, A

Reference 75

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

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Moment matching for multi-source domain adaptation,

Reference 76

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This paper cites A continual deepfake detection benchmark: Dataset, methods, and essentials,.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning A continual deepfake detection benchmark: Dataset, methods, and essentials,

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:12:15.730414Z

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

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Core50: a new dataset and benchmark for continuous object recognition,

Reference 78

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

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 79

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This paper cites Learning transferable visual models from natural language supervision,.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Learning transferable visual models from natural language supervision,

Reference 80

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This paper cites Dytox: Trans- formers for continual learning with dynamic token expansion,.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Dytox: Trans- formers for continual learning with dynamic token expansion,

Reference 81

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This paper cites Learning without forgetting,.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Learning without forgetting,

Reference 82

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This paper cites Learning to prompt for continual learning,.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Learning to prompt for continual learning,

Reference 83

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

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Dualprompt: Complementary prompting for rehearsal-free continual learning,

Reference 84

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

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Coda-prompt: Continual decomposed attention-based prompting for rehearsal-free continual learning,

Reference 85

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This paper cites Dual consolidation for pre-trained model-based domain-incremental learn- PREPRINT 14 ing,.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Dual consolidation for pre-trained model-based domain-incremental learn- PREPRINT 14 ing,

Reference 86

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This paper cites Dualcp: Rehearsal-free domain-incremental learning via dual-level concept prototype,.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Dualcp: Rehearsal-free domain-incremental learning via dual-level concept prototype,

Reference 87

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This paper cites Boosting domain incremental learning: Selecting the optimal parame- ters is all you need,.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Boosting domain incremental learning: Selecting the optimal parame- ters is all you need,

Reference 88

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Observation 60b78888-cc4b-432c-8028-35f79d410c1d · outbound

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

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Continual Knowledge Consolidation LORA for Domain Incremental Learning

Reference 89

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This paper cites Addressing imbal- anced domain-incremental learning through dual-balance collaborative experts,.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Addressing imbal- anced domain-incremental learning through dual-balance collaborative experts,

Reference 90

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This paper cites Prompt customization for continual learning,.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Prompt customization for continual learning,

Reference 91

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Observation 7e00fa0d-dada-4f2c-b759-e8928d168e97 · outbound

This paper cites Versatile incremental learning: Towards class and domain-agnostic incremental learning,.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Versatile incremental learning: Towards class and domain-agnostic incremental learning,

Reference 92

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Observation d1e914d3-2089-4d88-aaed-1a0fac013c39 · outbound

This paper cites Hierar- chical decomposition of prompt-based continual learning: Rethinking obscured sub-optimality,.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Hierar- chical decomposition of prompt-based continual learning: Rethinking obscured sub-optimality,

Reference 93

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This paper cites Cp-prompt: Composition-based cross-modal prompting for domain- incremental continual learning,.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Cp-prompt: Composition-based cross-modal prompting for domain- incremental continual learning,

Reference 94

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This paper cites Latent replay for real-time continual learning,.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Latent replay for real-time continual learning,

Reference 95

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Observation 576ddbe0-b73d-413f-bf1f-b9909c30fc13 · outbound

This paper cites Incremental learning for the detection and classification of gan-generated images,.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Incremental learning for the detection and classification of gan-generated images,

Reference 96

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This paper cites Learning a unified classifier incrementally via rebalancing,.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Learning a unified classifier incrementally via rebalancing,

Reference 97

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This paper cites On Tiny Episodic Memories in Continual Learning.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning On Tiny Episodic Memories in Continual Learning

Reference 98

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This paper cites Gdumb: A simple approach that questions our progress in continual learning,.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Gdumb: A simple approach that questions our progress in continual learning,

Reference 99

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Observation eddc5a2c-f9e4-42d9-bf3a-bdaa611f82e6 · outbound

This paper cites Large scale incremental learning,.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Large scale incremental learning,

Reference 100

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This paper cites Dark experience for general continual learning: a strong, simple baseline,.

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning Dark experience for general continual learning: a strong, simple baseline,

Reference 101

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