PROL : Rehearsal Free Continual Learning in Streaming Data via Prompt Online Learning | Paper Citation Record

Observation 02fe3d84-5e6b-4b7f-b330-286f3a63ad1e · outbound

This paper cites Continual Lifelong Learning in Natural Language Processing: A Survey.

PROL : Rehearsal Free Continual Learning in Streaming Data via Prompt Online Learning Continual Lifelong Learning in Natural Language Processing: A Survey

Reference 1

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Observation c4fdcce1-5551-41a0-98ae-bc377fea26c5 · outbound

This paper cites Class-incremental contin- ual learning into the extended der-verse.IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(5):5497– 5512, 2022.

PROL : Rehearsal Free Continual Learning in Streaming Data via Prompt Online Learning Class-incremental contin- ual learning into the extended der-verse.IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(5):5497– 5512, 2022

Reference 2

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This paper cites Dark experience for gen- eral continual learning: a strong, simple baseline.Advances in neural information processing systems, 33:15920–15930,.

PROL : Rehearsal Free Continual Learning in Streaming Data via Prompt Online Learning Dark experience for gen- eral continual learning: a strong, simple baseline.Advances in neural information processing systems, 33:15920–15930,

Reference 3

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Observation 8590d4b5-92fa-48d2-b585-3870a776c775 · outbound

This paper cites New Insights on Reducing Abrupt Representation Change in Online Continual Learning.

PROL : Rehearsal Free Continual Learning in Streaming Data via Prompt Online Learning New Insights on Reducing Abrupt Representation Change in Online Continual Learning

Reference 4

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Observation 92477a17-1166-44f4-acca-d7cbb527bcad · outbound

This paper cites A continual learning survey: Defying for- getting in classification tasks.IEEE transactions on pattern analysis and machine intelligence, 44(7):3366–3385, 2021.

PROL : Rehearsal Free Continual Learning in Streaming Data via Prompt Online Learning A continual learning survey: Defying for- getting in classification tasks.IEEE transactions on pattern analysis and machine intelligence, 44(7):3366–3385, 2021

Reference 5

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Observation 852f3ff0-2ea5-4e8f-92b6-afa8bd30ca47 · outbound

This paper cites A unified continual learn- ing framework with general parameter-efficient tuning.

PROL : Rehearsal Free Continual Learning in Streaming Data via Prompt Online Learning A unified continual learn- ing framework with general parameter-efficient tuning

Reference 6

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Observation e79483d1-f947-4cbb-bf84-efcbdce72d4a · outbound

This paper cites Consistent prompting for rehearsal-free continual learning.

PROL : Rehearsal Free Continual Learning in Streaming Data via Prompt Online Learning Consistent prompting for rehearsal-free continual learning

Reference 7

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Observation df829f8d-6aa2-46d8-8df7-bb594f8b2cc8 · outbound

This paper cites Not just selection, but exploration: Online class-incremental contin- ual learning via dual view consistency.

PROL : Rehearsal Free Continual Learning in Streaming Data via Prompt Online Learning Not just selection, but exploration: Online class-incremental contin- ual learning via dual view consistency

Reference 8

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Observation 6d272201-ff7c-42bd-888c-417f489e3ce0 · outbound

This paper cites Online continual learning through mutual information maximization.

PROL : Rehearsal Free Continual Learning in Streaming Data via Prompt Online Learning Online continual learning through mutual information maximization

Reference 9

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Observation e4540138-64a5-4244-a765-003e1a79b65e · outbound

This paper cites Dealing with cross-task class discrimination in online continual learning.

PROL : Rehearsal Free Continual Learning in Streaming Data via Prompt Online Learning Dealing with cross-task class discrimination in online continual learning

Reference 10

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Observation d0cd66d0-f231-4964-a279-cb86c166eca4 · outbound

This paper cites The many faces of robustness: A critical analysis of out-of-distribution generalization.ICCV, 2021.

PROL : Rehearsal Free Continual Learning in Streaming Data via Prompt Online Learning The many faces of robustness: A critical analysis of out-of-distribution generalization.ICCV, 2021

Reference 11

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Observation e31817c4-a617-48e8-af6d-88f84725117d · outbound

This paper cites Natural adversarial examples.CVPR,.

PROL : Rehearsal Free Continual Learning in Streaming Data via Prompt Online Learning Natural adversarial examples.CVPR,

Reference 12

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Observation 6c26ba6e-b3bb-4514-be14-58b0c165a209 · outbound

This paper cites Non-exemplar online class-incremental con- tinual learning via dual-prototype self-augment and refine- ment.

PROL : Rehearsal Free Continual Learning in Streaming Data via Prompt Online Learning Non-exemplar online class-incremental con- tinual learning via dual-prototype self-augment and refine- ment

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:55:04.146499Z

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Observation be07e703-573f-4b1c-af24-de1db28b7084 · outbound

This paper cites Online Continual Learning For Interactive Instruction Following Agents.

PROL : Rehearsal Free Continual Learning in Streaming Data via Prompt Online Learning Online Continual Learning For Interactive Instruction Following Agents

Reference 14

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Observation 5e613dd9-b2e7-44b4-bcbe-5aedd45dcc76 · outbound

This paper cites Online Continual Learning on Class Incremental Blurry Task Configuration with Anytime Inference.

PROL : Rehearsal Free Continual Learning in Streaming Data via Prompt Online Learning Online Continual Learning on Class Incremental Blurry Task Configuration with Anytime Inference

Reference 15

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Observation 4e896fde-5934-414a-862a-262db6982002 · outbound

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

PROL : Rehearsal Free Continual Learning in Streaming Data via Prompt Online Learning Learning multiple layers of features from tiny images

Reference 16

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Observation 5a01e903-85ad-47e0-9ef1-4a4ed4a1f11e · outbound

This paper cites Evolv- ing parameterized prompt memory for continual learning.

PROL : Rehearsal Free Continual Learning in Streaming Data via Prompt Online Learning Evolv- ing parameterized prompt memory for continual learning

Reference 17

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Observation e249c0a1-82ba-43ef-b8a4-1831782853b3 · outbound

This paper cites The Power of Scale for Parameter-Efficient Prompt Tuning.

PROL : Rehearsal Free Continual Learning in Streaming Data via Prompt Online Learning The Power of Scale for Parameter-Efficient Prompt Tuning

Reference 18

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Observation a20fe72a-6bc6-4e33-9d11-8b232a17f687 · outbound

This paper cites Prefix-Tuning: Optimizing Continuous Prompts for Generation.

PROL : Rehearsal Free Continual Learning in Streaming Data via Prompt Online Learning Prefix-Tuning: Optimizing Continuous Prompts for Generation

Reference 19

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Observation 3b052d58-f911-4635-9054-6c02216ec446 · outbound

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

PROL : Rehearsal Free Continual Learning in Streaming Data via Prompt Online Learning Inflora: Interference-free low-rank adaptation for continual learning

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:55:03.769908Z

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Observation 8fe690ca-e56f-4712-bab4-b60015509345 · outbound

This paper cites Incremental learning with neural networks for computer vision: a survey.Artificial intelligence review, 56 (5):4557–4589, 2023.

PROL : Rehearsal Free Continual Learning in Streaming Data via Prompt Online Learning Incremental learning with neural networks for computer vision: a survey.Artificial intelligence review, 56 (5):4557–4589, 2023

Reference 21

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Observation af0d4132-50ab-4e1a-badf-51eb52a00e50 · outbound

This paper cites Ranpac: Ran- dom projections and pre-trained models for continual learn- ing.Advances in Neural Information Processing Systems, 36:12022–12053, 2023.

PROL : Rehearsal Free Continual Learning in Streaming Data via Prompt Online Learning Ranpac: Ran- dom projections and pre-trained models for continual learn- ing.Advances in Neural Information Processing Systems, 36:12022–12053, 2023

Reference 22

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Observation 858dab6c-53e7-48d2-9458-73af679db46a · outbound

This paper cites Rethinking momentum knowledge distillation in online continual learning.

PROL : Rehearsal Free Continual Learning in Streaming Data via Prompt Online Learning Rethinking momentum knowledge distillation in online continual learning

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:55:03.163930Z

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Observation b2816fb8-8a5a-4bbd-823b-b4c97526ac95 · outbound

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

PROL : Rehearsal Free Continual Learning in Streaming Data via Prompt Online Learning Gdumb: A simple approach that questions our progress in continual learning

Reference 24

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Observation ab4ea987-a77a-474c-a383-e8764a9cd256 · outbound

This paper cites Ran- dumb: Random representations outperform online continu- ally learned representations.Advances in Neural Informa- tion Processing Systems, 37:37988–38006, 2024.

PROL : Rehearsal Free Continual Learning in Streaming Data via Prompt Online Learning Ran- dumb: Random representations outperform online continu- ally learned representations.Advances in Neural Informa- tion Processing Systems, 37:37988–38006, 2024

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:55:02.495583Z

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Observation d2698876-a54e-44eb-a33f-2d550201a472 · outbound

This paper cites icarl: Incremental classifier and representation learning.

PROL : Rehearsal Free Continual Learning in Streaming Data via Prompt Online Learning icarl: Incremental classifier and representation learning

Reference 26

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Observation b4de9cd8-178a-4145-9864-4c72fcd5da6a · outbound

This paper cites Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference.

PROL : Rehearsal Free Continual Learning in Streaming Data via Prompt Online Learning Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference

Reference 27

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Observation 57e8ec49-c1da-4683-9595-71d50b92738a · outbound

This paper cites Convolutional prompting meets language models for continual learning.

PROL : Rehearsal Free Continual Learning in Streaming Data via Prompt Online Learning Convolutional prompting meets language models for continual learning

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:55:01.383975Z

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Observation 4428fbc0-b08f-46d3-9919-841d927ac54f · outbound

This paper cites Learning equi-angular repre- sentations for online continual learning.

PROL : Rehearsal Free Continual Learning in Streaming Data via Prompt Online Learning Learning equi-angular repre- sentations for online continual learning

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:55:01.089074Z

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Observation 801c93c4-f5d1-44e1-93df-64631e1f9773 · outbound

This paper cites Bud- geted online continual learning by adaptive layer freezing and frequency-based sampling.

PROL : Rehearsal Free Continual Learning in Streaming Data via Prompt Online Learning Bud- geted online continual learning by adaptive layer freezing and frequency-based sampling

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:55:00.803064Z

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Observation 3aa56c9c-7e6a-4120-85db-b61babf02dd6 · outbound

This paper cites Continual learning for real-world autonomous systems: Algorithms, challenges and frameworks.Journal of Intelligent & Robotic Systems, 105 (1):9, 2022.

PROL : Rehearsal Free Continual Learning in Streaming Data via Prompt Online Learning Continual learning for real-world autonomous systems: Algorithms, challenges and frameworks.Journal of Intelligent & Robotic Systems, 105 (1):9, 2022

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:55:00.475186Z

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Observation d3059f3d-5446-45d0-beb7-cb43a16d4d2a · outbound

This paper cites Continual Diffusion: Continual Customization of Text-to-Image Diffusion with C-LoRA.

PROL : Rehearsal Free Continual Learning in Streaming Data via Prompt Online Learning Continual Diffusion: Continual Customization of Text-to-Image Diffusion with C-LoRA

Reference 32

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Observation 7044ec8c-f167-452a-94a8-aa0bc10987e5 · outbound

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

PROL : Rehearsal Free Continual Learning in Streaming Data via Prompt Online Learning Coda-prompt: Contin- ual decomposed attention-based prompting for rehearsal-free continual learning

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:55:00.133441Z

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Observation 07c6469a-9bf9-4564-be11-7180c764cdbb · outbound

This paper cites MOS: Model Surgery for Pre-Trained Model-Based Class-Incremental Learning.

PROL : Rehearsal Free Continual Learning in Streaming Data via Prompt Online Learning MOS: Model Surgery for Pre-Trained Model-Based Class-Incremental Learning

Reference 34

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Observation 41cc0d96-55af-44b1-80a9-3746f38a039b · outbound

This paper cites Continual Learning on Graphs: A Survey.

PROL : Rehearsal Free Continual Learning in Streaming Data via Prompt Online Learning Continual Learning on Graphs: A Survey

Reference 35

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Observation 302794ef-25a6-4ff2-bfda-37d8e79779b2 · outbound

This paper cites The caltech-ucsd birds-200-2011 dataset.

PROL : Rehearsal Free Continual Learning in Streaming Data via Prompt Online Learning The caltech-ucsd birds-200-2011 dataset

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:54:59.816446Z

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Observation 6a23f9cd-72cc-4151-abb5-49ce5d6c94c7 · outbound

This paper cites Hierarchical decomposition of prompt-based continual learning: Rethinking obscured sub- optimality.Advances in Neural Information Processing Sys- tems, 36, 2024.

PROL : Rehearsal Free Continual Learning in Streaming Data via Prompt Online Learning Hierarchical decomposition of prompt-based continual learning: Rethinking obscured sub- optimality.Advances in Neural Information Processing Sys- tems, 36, 2024

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:54:59.515272Z

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Observation ae0de4ff-c0a8-4f26-882e-3b2b7aad1874 · outbound

This paper cites A comprehensive survey of continual learning: theory, method and application.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024.

PROL : Rehearsal Free Continual Learning in Streaming Data via Prompt Online Learning A comprehensive survey of continual learning: theory, method and application.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:54:59.293371Z

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Observation 0559e2fb-ad40-440e-981d-11685fba272f · outbound

This paper cites Dealing with synthetic data contamination in on- line continual learning.

PROL : Rehearsal Free Continual Learning in Streaming Data via Prompt Online Learning Dealing with synthetic data contamination in on- line continual learning

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:54:59.083714Z

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Observation a336dc6e-2497-42eb-8fda-1e23794a4743 · outbound

This paper cites Improving plasticity in online continual learning via collaborative learning.

PROL : Rehearsal Free Continual Learning in Streaming Data via Prompt Online Learning Improving plasticity in online continual learning via collaborative learning

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:54:58.829594Z

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Observation a7393544-c556-4154-8376-c5ca5784ec10 · outbound

This paper cites S-prompts learning with pre-trained transformers: An occam’s razor for domain incremental learning.Advances in Neural Informa- tion Processing Systems, 35:5682–5695, 2022.

PROL : Rehearsal Free Continual Learning in Streaming Data via Prompt Online Learning S-prompts learning with pre-trained transformers: An occam’s razor for domain incremental learning.Advances in Neural Informa- tion Processing Systems, 35:5682–5695, 2022

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:54:58.599459Z

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Observation f73676c4-f19e-4290-927b-e717c1b90058 · outbound

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

PROL : Rehearsal Free Continual Learning in Streaming Data via Prompt Online Learning Dualprompt: Complementary prompting for rehearsal-free continual learning

Reference 43

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Observation d1c8631f-de7f-4206-b09b-a68651c21aba · outbound

This paper cites Learning to prompt for continual learning.

PROL : Rehearsal Free Continual Learning in Streaming Data via Prompt Online Learning Learning to prompt for continual learning

Reference 44

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Observation e6ea4554-f167-47b4-9e1c-a56d102fc962 · outbound

This paper cites Online prototype learning for online con- tinual learning.

PROL : Rehearsal Free Continual Learning in Streaming Data via Prompt Online Learning Online prototype learning for online con- tinual learning

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:54:58.132003Z

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Observation 4fd9ace0-3c15-4081-b5da-6c6d1f12ca1c · outbound

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PROL : Rehearsal Free Continual Learning in Streaming Data via Prompt Online Learning Unresolved cited work

Reference 46

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This paper cites Mitigating catastrophic forget- ting in online continual learning by modeling previous task interrelations via pareto optimization.

PROL : Rehearsal Free Continual Learning in Streaming Data via Prompt Online Learning Mitigating catastrophic forget- ting in online continual learning by modeling previous task interrelations via pareto optimization

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:54:57.669190Z

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Observation ca444e6b-7b76-4058-b183-e6e0eb60f723 · outbound

This paper cites Large scale in- cremental learning.

PROL : Rehearsal Free Continual Learning in Streaming Data via Prompt Online Learning Large scale in- cremental learning

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:54:57.895325Z

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Observation 39fe83f2-193f-43d8-bbf9-98f8df31a435 · outbound

This paper cites Layerwise proximal replay: a proximal point method for on- line continual learning.

PROL : Rehearsal Free Continual Learning in Streaming Data via Prompt Online Learning Layerwise proximal replay: a proximal point method for on- line continual learning

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:54:57.295870Z

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Observation c41ef5b1-5531-4329-b4e7-e4881822c9fa · outbound

This paper cites Forgetting, ignorance or myopia: Revis- iting key challenges in online continual learning.Advances in Neural Information Processing Systems, 37:58341–58375,.

PROL : Rehearsal Free Continual Learning in Streaming Data via Prompt Online Learning Forgetting, ignorance or myopia: Revis- iting key challenges in online continual learning.Advances in Neural Information Processing Systems, 37:58341–58375,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:54:57.457653Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ddc5e72e-fb45-4708-8e9b-7624202d5802 · outbound

This paper cites Safe: Slow and fast parameter- efficient tuning for continual learning with pre-trained mod- els.Advances in Neural Information Processing Systems, 37: 113772–113796, 2025.

PROL : Rehearsal Free Continual Learning in Streaming Data via Prompt Online Learning Safe: Slow and fast parameter- efficient tuning for continual learning with pre-trained mod- els.Advances in Neural Information Processing Systems, 37: 113772–113796, 2025

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:54:56.829312Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 41fda3e8-778b-4f15-916c-6d6315e7b072 · outbound

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

PROL : Rehearsal Free Continual Learning in Streaming Data via Prompt Online Learning Slca: Slow learner with classifier align- ment for continual learning on a pre-trained model

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:54:57.037246Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation cb72b3bb-a5f5-40e9-a86a-3dce3a477ff9 · outbound

This paper cites F-oal: Forward-only online analytic learning with fast training and low memory footprint in class incremental learning.

PROL : Rehearsal Free Continual Learning in Streaming Data via Prompt Online Learning F-oal: Forward-only online analytic learning with fast training and low memory footprint in class incremental learning

Reference 53

Resolution: malformed identifier; raw_fallback, observed 2026-08-06T16:54:56.526416Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation bc75ee1f-092a-45b8-b620-5a47a821565b · outbound

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

PROL : Rehearsal Free Continual Learning in Streaming Data via Prompt Online Learning Expandable subspace ensemble for pre-trained model- based class-incremental learning

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:54:56.704013Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ddf06799-8916-4e6e-b960-de851273072e · outbound

This paper cites an unresolved cited work.

PROL : Rehearsal Free Continual Learning in Streaming Data via Prompt Online Learning Unresolved cited work

Reference 2022

Resolution: parse uncertain; raw_fallback, observed 2026-08-06T16:54:58.379093Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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