Dealing with Synthetic Data Contamination in Online Continual Learning | Paper Citation Record

Observation 1380df0e-8539-4002-9965-3fd837048354 · outbound

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

Dealing with Synthetic Data Contamination in Online Continual Learning Memory aware synapses: Learning what (not) to forget

Reference 1

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Observation 924a1329-9d14-4ed7-b2a7-4473d0b4cd8e · outbound

This paper cites Online continual learning with maximal interfered retrieval.

Dealing with Synthetic Data Contamination in Online Continual Learning Online continual learning with maximal interfered retrieval

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:53:14.643337Z

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Observation 04484083-88d0-4de2-96a2-8e0d29ea75b4 · outbound

This paper cites Expert gate: Lifelong learning with a network of experts.

Dealing with Synthetic Data Contamination in Online Continual Learning Expert gate: Lifelong learning with a network of experts

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:53:14.634597Z

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Observation 6bb5e29c-44ac-431e-9c57-6ed2430a9557 · outbound

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

Dealing with Synthetic Data Contamination in Online Continual Learning Gradient based sample selection for online continual learning

Reference 4

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Observation 5f02ff2a-d845-4146-97b7-a97467c24f38 · outbound

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

Dealing with Synthetic Data Contamination in Online Continual Learning Dark experience for general continual learning: a strong, simple baseline

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:53:14.617106Z

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Observation fb338e4a-f7f1-4934-bda2-cf9ce7551e1a · outbound

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

Dealing with Synthetic Data Contamination in Online Continual Learning New Insights on Reducing Abrupt Representation Change in Online Continual Learning

Reference 6

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Observation bd6ad1d3-1008-4b32-aeda-4d17b428b90e · outbound

This paper cites Riemannian walk for incremental learning: Understanding forgetting and intransigence.

Dealing with Synthetic Data Contamination in Online Continual Learning Riemannian walk for incremental learning: Understanding forgetting and intransigence

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:53:14.607933Z

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Observation 451249d1-3f33-4b69-930d-a69cf5b2f738 · outbound

This paper cites Would Deep Generative Models Amplify Bias in Future Models?.

Dealing with Synthetic Data Contamination in Online Continual Learning Would Deep Generative Models Amplify Bias in Future Models?

Reference 8

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Observation f6ca6f38-8c00-42a7-8dbd-0453d62122a0 · outbound

This paper cites A simple framework for contrastive learning of visual representations.

Dealing with Synthetic Data Contamination in Online Continual Learning A simple framework for contrastive learning of visual representations

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:53:14.599251Z

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Observation 0eadefd7-c64e-48ff-9f3d-3ee26965b253 · outbound

This paper cites Lifelong machine learning.

Dealing with Synthetic Data Contamination in Online Continual Learning Lifelong machine learning

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:53:14.590644Z

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Observation e12b2751-81c5-41a7-947d-879b1484ebb5 · outbound

This paper cites IN100pytorch: Pytorch implementation: Training resnets on imagenet-100.

Dealing with Synthetic Data Contamination in Online Continual Learning IN100pytorch: Pytorch implementation: Training resnets on imagenet-100

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:53:14.582038Z

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Observation b83e4789-f338-40f1-b9b1-e940ab9e8f58 · outbound

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

Dealing with Synthetic Data Contamination in Online Continual Learning A continual learning survey: Defying forgetting in classification tasks

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:53:14.572949Z

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Observation 725954a4-b26f-4038-87a2-ffa863e1ad9d · outbound

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

Dealing with Synthetic Data Contamination in Online Continual Learning Imagenet: A large-scale hierarchical image database

Reference 13

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Observation 0a02c63d-863b-4bb8-a679-346629bbca84 · outbound

This paper cites PathNet: Evolution Channels Gradient Descent in Super Neural Networks.

Dealing with Synthetic Data Contamination in Online Continual Learning PathNet: Evolution Channels Gradient Descent in Super Neural Networks

Reference 14

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Observation ed4ee023-c44b-40bc-9eeb-eedf6537ad4e · outbound

This paper cites Vector quantized diffusion model for text-to-image synthesis.

Dealing with Synthetic Data Contamination in Online Continual Learning Vector quantized diffusion model for text-to-image synthesis

Reference 15

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Observation 9aebdfa5-7440-4512-8458-b696ae9d20d9 · outbound

This paper cites Online continual learning through mutual information maximization.

Dealing with Synthetic Data Contamination in Online Continual Learning Online continual learning through mutual information maximization

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:53:14.554333Z

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Observation d3c869b3-507a-438b-93de-4c14184b873a · outbound

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

Dealing with Synthetic Data Contamination in Online Continual Learning Dealing with cross-task class discrimination in online continual learning

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:53:14.544922Z

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Observation 73cc1bc3-2afa-46a4-878e-2ee19aa69d0a · outbound

This paper cites Will large-scale generative models corrupt future datasets? In ICCV, pages 20555–20565, 2023.

Dealing with Synthetic Data Contamination in Online Continual Learning Will large-scale generative models corrupt future datasets? In ICCV, pages 20555–20565, 2023

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:53:14.536212Z

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Observation 429154cd-a09c-4c3e-ac3a-789116ea9fff · outbound

This paper cites Denoising diffusion probabilistic models.

Dealing with Synthetic Data Contamination in Online Continual Learning Denoising diffusion probabilistic models

Reference 19

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Observation 4034e01b-5d9f-4cc3-8b66-473965734f7f · outbound

This paper cites Learning a unified classifier incrementally via rebalancing.

Dealing with Synthetic Data Contamination in Online Continual Learning Learning a unified classifier incrementally via rebalancing

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:53:14.521687Z

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Observation 46a730fd-1238-467e-9e39-3568b4bb3ae1 · outbound

This paper cites Selective experience replay for lifelong learning.

Dealing with Synthetic Data Contamination in Online Continual Learning Selective experience replay for lifelong learning

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:53:14.511146Z

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Observation 7892310a-5094-4149-b041-d5c1196da2de · outbound

This paper cites Supervised contrastive learning.

Dealing with Synthetic Data Contamination in Online Continual Learning Supervised contrastive learning

Reference 22

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Observation 41ae5bb3-9adc-4acd-bf39-365e1bf866f6 · outbound

This paper cites Overcoming catastrophic forgetting in neural networks.

Dealing with Synthetic Data Contamination in Online Continual Learning Overcoming catastrophic forgetting in neural networks

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:53:14.493135Z

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Observation 9b1b28a6-07c1-442b-9e0e-1a6177ade78f · outbound

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

Dealing with Synthetic Data Contamination in Online Continual Learning Learning multiple layers of features from tiny images

Reference 24

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Observation 21866773-904e-4bc7-ab9d-50ed667eabbc · outbound

This paper cites Tiny imagenet visual recognition challenge.

Dealing with Synthetic Data Contamination in Online Continual Learning Tiny imagenet visual recognition challenge

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:53:14.478937Z

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Observation c86328aa-5708-419e-b1d9-911cf91416c7 · outbound

This paper cites Overcoming catastrophic forgetting by incremental moment matching.

Dealing with Synthetic Data Contamination in Online Continual Learning Overcoming catastrophic forgetting by incremental moment matching

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:53:14.469597Z

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Observation 10da2226-b725-40e1-acdc-b2b9f60046a5 · outbound

This paper cites Forgery-aware adaptive transformer for generalizable synthetic image detection.

Dealing with Synthetic Data Contamination in Online Continual Learning Forgery-aware adaptive transformer for generalizable synthetic image detection

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:53:14.460087Z

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Observation 04b0522e-b3d8-4afd-9423-42ceaf46d26c · outbound

This paper cites Towards understanding the interplay of generative artificial intelligence and the internet.

Dealing with Synthetic Data Contamination in Online Continual Learning Towards understanding the interplay of generative artificial intelligence and the internet

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:53:14.451086Z

Source-reported events for the cited work

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Observation 75c98934-1209-4d0f-83fd-98396f218d34 · outbound

This paper cites GLIDE: Towards photorealistic image generation and editing with text-guided diffusion models.

Dealing with Synthetic Data Contamination in Online Continual Learning GLIDE: Towards photorealistic image generation and editing with text-guided diffusion models

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:53:14.441746Z

Source-reported events for the cited work

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Observation 5db37e3f-9486-41df-bf91-c7545288d931 · outbound

This paper cites Towards universal fake image detectors that generalize across generative models.

Dealing with Synthetic Data Contamination in Online Continual Learning Towards universal fake image detectors that generalize across generative models

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:53:14.432868Z

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Observation ba80271b-952d-45e7-89ff-d38b93a4b542 · outbound

This paper cites Continual lifelong learning with neural networks: A review.

Dealing with Synthetic Data Contamination in Online Continual Learning Continual lifelong learning with neural networks: A review

Reference 31

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

Dealing with Synthetic Data Contamination in Online Continual Learning Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-12T15:53:14.018346Z

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Observation c2e0b37f-9023-4924-a0fc-71d1c02e8111 · outbound

This paper cites Experi- ence replay for continual learning.

Dealing with Synthetic Data Contamination in Online Continual Learning Experi- ence replay for continual learning

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:53:14.419672Z

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Observation e7fd3b9c-bd71-462b-b501-219341999eaa · outbound

This paper cites High- resolution image synthesis with latent diffusion models.

Dealing with Synthetic Data Contamination in Online Continual Learning High- resolution image synthesis with latent diffusion models

Reference 34

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Observation 4a597488-d462-445a-bec6-1ac3dec6a550 · outbound

This paper cites Incremental learning through deep adaptation.

Dealing with Synthetic Data Contamination in Online Continual Learning Incremental learning through deep adaptation

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:53:14.406266Z

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Observation 3e1d364b-b9e5-420c-9208-b845f8c5e988 · outbound

This paper cites Progressive Neural Networks.

Dealing with Synthetic Data Contamination in Online Continual Learning Progressive Neural Networks

Reference 36

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Observation 91b554ac-eeed-4117-9fae-02194580132a · outbound

This paper cites Overcoming catastrophic forgetting with hard attention to the task.

Dealing with Synthetic Data Contamination in Online Continual Learning Overcoming catastrophic forgetting with hard attention to the task

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:53:14.397708Z

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Observation 5c2b4c0c-eb37-4ce2-9160-d0b309f7679e · outbound

This paper cites Three scenarios for continual learning.

Dealing with Synthetic Data Contamination in Online Continual Learning Three scenarios for continual learning

Reference 38

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Observation fe9bfb53-d66a-4f5f-ac53-9f8d2d38a2ed · outbound

This paper cites Visualizing data using t-sne.

Dealing with Synthetic Data Contamination in Online Continual Learning Visualizing data using t-sne

Reference 39

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Observation b5766b7a-4c84-426a-a4dd-81e0ab4e846b · outbound

This paper cites Random sampling with a reservoir.

Dealing with Synthetic Data Contamination in Online Continual Learning Random sampling with a reservoir

Reference 40

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Observation 12a9f7a2-dde5-474c-a5b5-cc499e143fc0 · outbound

This paper cites A Comprehensive Survey of Continual Learning: Theory, Method and Application.

Dealing with Synthetic Data Contamination in Online Continual Learning A Comprehensive Survey of Continual Learning: Theory, Method and Application

Reference 41

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Observation 2ad12577-983a-4f41-8bde-829afe040818 · outbound

This paper cites Improving Plasticity in Online Continual Learning via Collaborative Learning.

Dealing with Synthetic Data Contamination in Online Continual Learning Improving Plasticity in Online Continual Learning via Collaborative Learning

Reference 42

Resolution: verified exact; local_arxiv, observed 2026-08-12T15:53:14.141479Z

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Observation c57afa46-4bc2-4f23-92ec-61df3ce3093b · outbound

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

Dealing with Synthetic Data Contamination in Online Continual Learning Dualprompt: Complementary prompting for rehearsal-free continual learning

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:53:14.379490Z

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Observation 396c93a5-8425-43d0-aee4-d5f526673123 · outbound

This paper cites Learning to prompt for continual learning.

Dealing with Synthetic Data Contamination in Online Continual Learning Learning to prompt for continual learning

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:53:14.369915Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 18aebb9c-4f6b-45ba-b32a-d98b5b124f48 · outbound

This paper cites Online prototype learning for online continual learning.

Dealing with Synthetic Data Contamination in Online Continual Learning Online prototype learning for online continual learning

Reference 45

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Observation 8addbe06-174f-4b25-a4da-a7427e58fb3c · outbound

This paper cites an image of a <class_name>.

Dealing with Synthetic Data Contamination in Online Continual Learning an image of a <class_name>

Reference 46

Resolution: malformed identifier; raw_fallback, observed 2026-08-12T15:53:14.352851Z

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Observation 4eb675db-c9b7-4e73-a49f-064c3deafce0 · outbound

This paper cites The partial augmentation is a weaker version of augmentation, consisting of random cropping with p = 0.5, followed by random horizontal flip with p = 0.5.

Dealing with Synthetic Data Contamination in Online Continual Learning The partial augmentation is a weaker version of augmentation, consisting of random cropping with p = 0.5, followed by random horizontal flip with p = 0.5

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:53:14.344446Z

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Observation 87217b79-bc6e-4e36-8637-8aa755e1b666 · outbound

This paper cites The full augmentation strategy is a stronger version of augmentation.

Dealing with Synthetic Data Contamination in Online Continual Learning The full augmentation strategy is a stronger version of augmentation

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:53:14.336239Z

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Observation b55c0cb4-17ee-42db-aba3-92214a6fccfb · outbound

This paper cites 22 Guidelines: • The answer NA means that the abstract and introduction do not include the claims made in the paper.

Dealing with Synthetic Data Contamination in Online Continual Learning 22 Guidelines: • The answer NA means that the abstract and introduction do not include the claims made in the paper

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:53:14.327924Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 5e926ec3-de04-481c-b0fa-c5220a3acdf3 · outbound

This paper cites Limitations.

Dealing with Synthetic Data Contamination in Online Continual Learning Limitations

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:53:14.319299Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 72198b58-9857-416c-aa57-e2e452419810 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not include theoretical results.

Dealing with Synthetic Data Contamination in Online Continual Learning Guidelines: • The answer NA means that the paper does not include theoretical results

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:53:14.310998Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 906aaf06-9b51-48f1-a5b5-3734048b7df3 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not include experiments.

Dealing with Synthetic Data Contamination in Online Continual Learning Guidelines: • The answer NA means that the paper does not include experiments

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:53:14.302492Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 9a9b88c0-12f6-4778-98ee-30e60db15f77 · outbound

This paper cites Guidelines: • The answer NA means that paper does not include experiments requiring code.

Dealing with Synthetic Data Contamination in Online Continual Learning Guidelines: • The answer NA means that paper does not include experiments requiring code

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:53:14.293919Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 555d8382-f06e-492f-bcad-fd1c2d7d811f · outbound

This paper cites Guidelines: • The answer NA means that the paper does not include experiments.

Dealing with Synthetic Data Contamination in Online Continual Learning Guidelines: • The answer NA means that the paper does not include experiments

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:53:14.285028Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 8917f7b2-cdaf-4df5-ab01-e7a6e74ab9c7 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not include experiments.

Dealing with Synthetic Data Contamination in Online Continual Learning Guidelines: • The answer NA means that the paper does not include experiments

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:53:14.276736Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 07dcf9a2-7dc0-46da-8386-712155aae2ee · outbound

This paper cites Guidelines: • The answer NA means that the paper does not include experiments.

Dealing with Synthetic Data Contamination in Online Continual Learning Guidelines: • The answer NA means that the paper does not include experiments

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:53:14.268296Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 22f0920d-0710-41e7-a540-3a8d48aee92c · outbound

This paper cites Guidelines: • The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics.

Dealing with Synthetic Data Contamination in Online Continual Learning Guidelines: • The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:53:14.258883Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation e70caf06-1af1-43a6-a642-e3146864b30b · outbound

This paper cites Guidelines: • The answer NA means that there is no societal impact of the work performed.

Dealing with Synthetic Data Contamination in Online Continual Learning Guidelines: • The answer NA means that there is no societal impact of the work performed

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:53:14.249582Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 05421a5c-ad2f-428d-9b5d-7656ba04f89a · outbound

This paper cites Guidelines: • The answer NA means that the paper poses no such risks.

Dealing with Synthetic Data Contamination in Online Continual Learning Guidelines: • The answer NA means that the paper poses no such risks

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-12T15:53:14.102970Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7119ca27-fb71-4de7-903d-71293486f784 · outbound

This paper cites The licenses of existing assets are properly respected.

Dealing with Synthetic Data Contamination in Online Continual Learning The licenses of existing assets are properly respected

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:53:14.235801Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 95dbac65-a9fa-4b22-bd4d-b0e708024ce1 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not release new assets.

Dealing with Synthetic Data Contamination in Online Continual Learning Guidelines: • The answer NA means that the paper does not release new assets

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:53:14.227175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 49937291-ed55-4c1a-9016-fd48cb1dc0d5 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

Dealing with Synthetic Data Contamination in Online Continual Learning Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:53:14.218346Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation a9b95656-f4a9-4384-9aef-5e5ab39e5112 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

Dealing with Synthetic Data Contamination in Online Continual Learning Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:53:14.209268Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers