PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization | Paper Citation Record

Observation 107f3cc5-6b56-44cd-8fcb-03bf7bde8e14 · outbound

This paper cites Ainsworth, Jonathan Hayase, and Siddhartha Srini- vasa.

PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization Ainsworth, Jonathan Hayase, and Siddhartha Srini- vasa

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:31:55.502339Z

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Observation 3151a901-2be6-4e88-801b-be22f97b20a6 · outbound

This paper cites Geometric dataset dis- tances via optimal transport.Advances in Neural Information Processing Systems, 33:21428–21439, 2020.

PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization Geometric dataset dis- tances via optimal transport.Advances in Neural Information Processing Systems, 33:21428–21439, 2020

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:31:55.488359Z

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Observation 8aa3d7c4-ce17-446a-82c3-cf0f5627a203 · outbound

This paper cites Guerrero Peña, Heitor Rapela Medeiros, Thomas Dubail, Eric Granger, and Marco Pedersoli.

PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization Guerrero Peña, Heitor Rapela Medeiros, Thomas Dubail, Eric Granger, and Marco Pedersoli

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:31:55.473941Z

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Observation 8fcdaf91-10a5-4080-9cdb-f9a89387a701 · outbound

This paper cites Invariant risk minimization, 2019.

PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization Invariant risk minimization, 2019

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:31:55.459674Z

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Observation f5c09cb6-eea2-41d6-95ce-57a925933d33 · outbound

This paper cites Ensemble of averages: Improving model selection and boosting performance in domain generalization.Advances in Neural Information Processing Systems, 35:8265–8277, 2022.

PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization Ensemble of averages: Improving model selection and boosting performance in domain generalization.Advances in Neural Information Processing Systems, 35:8265–8277, 2022

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:31:55.445712Z

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Observation ce605db7-b9cf-4f00-96d6-8b759e9466bd · outbound

This paper cites A cookbook of self-supervised learning, 2023.

PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization A cookbook of self-supervised learning, 2023

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:31:55.431411Z

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Observation 3351d500-f0db-43ea-bb47-d5cf91e71129 · outbound

This paper cites Knowledge distilla- tion: A good teacher is patient and consistent, 2022.

PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization Knowledge distilla- tion: A good teacher is patient and consistent, 2022

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:31:55.416476Z

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Observation fd80aeb4-3dba-40eb-81f0-1ca285ce19d8 · outbound

This paper cites Weak-to-strong generalization: Eliciting strong capabilities with weak supervision, 2023.

PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization Weak-to-strong generalization: Eliciting strong capabilities with weak supervision, 2023

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:31:55.402461Z

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Observation f8bb760d-3a78-49e7-9e72-a0f33ff93c15 · outbound

This paper cites Domain Generalization by Mutual-Information Regularization with Pre-trained Models.

PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization Domain Generalization by Mutual-Information Regularization with Pre-trained Models

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-15T20:31:54.041421Z

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Observation cdb9f414-8cb1-4aed-bcc8-1c3fe611c722 · outbound

This paper cites Fusing finetuned models for better pretraining.

PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization Fusing finetuned models for better pretraining

Reference 10

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Observation 532c301a-d955-44f2-95c4-aab0a04a497c · outbound

This paper cites Randaugment: Practical automated data augmentation with a reduced search space.

PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization Randaugment: Practical automated data augmentation with a reduced search space

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:31:55.388505Z

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Observation e325f9f9-53e0-43c6-a60c-1d58e5129183 · outbound

This paper cites The mnist database of handwritten digit images for machine learning research.IEEE Signal Processing Maga- zine, 29(6):141–142, 2012.

PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization The mnist database of handwritten digit images for machine learning research.IEEE Signal Processing Maga- zine, 29(6):141–142, 2012

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:31:55.375009Z

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Observation b1b8a956-824b-4180-8c29-098c263362c7 · outbound

This paper cites Crafting Distribution Shifts for Validation and Training in Single Source Domain Generalization.

PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization Crafting Distribution Shifts for Validation and Training in Single Source Domain Generalization

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-08-15T20:31:54.585468Z

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Observation 1184e695-3ad2-4780-874c-ad85e364148a · outbound

This paper cites The Role of Permutation Invariance in Linear Mode Connectivity of Neural Networks.

PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization The Role of Permutation Invariance in Linear Mode Connectivity of Neural Networks

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-15T20:31:54.065209Z

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Observation 9ac9e451-f2fd-448c-9cd2-51158e4c91b9 · outbound

This paper cites https://torchvision.mlverse.org, https://github.com/mlverse/torchvision.

PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization https://torchvision.mlverse.org, https://github.com/mlverse/torchvision

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:31:55.361634Z

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Observation ba6157d3-4371-4d36-aad0-1fbd1524cead · outbound

This paper cites Adversarially adaptive normal- ization for single domain generalization.

PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization Adversarially adaptive normal- ization for single domain generalization

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:31:55.348257Z

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Observation 74c061c0-75d3-42f7-b262-ca49ff4ebeb6 · outbound

This paper cites Unbiased met- ric learning: On the utilization of multiple datasets and web images for softening bias.

PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization Unbiased met- ric learning: On the utilization of multiple datasets and web images for softening bias

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:31:55.334568Z

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Observation b9cbad7f-3870-41ea-9404-8418876266ae · outbound

This paper cites Linear mode connectivity and the lottery ticket hypothesis.

PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization Linear mode connectivity and the lottery ticket hypothesis

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:31:55.320753Z

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Observation 662ef89e-080f-4d6c-ae03-342b3dfb953d · outbound

This paper cites Unsupervised domain adaptation by backpropagation.

PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization Unsupervised domain adaptation by backpropagation

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:31:55.306431Z

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Observation cfafda48-cdcc-467c-ae04-58b57a861a48 · outbound

This paper cites Domain-adversarial training of neural networks.Journal of Machine Learning Research 17 (2016) 1-35, 2015.

PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization Domain-adversarial training of neural networks.Journal of Machine Learning Research 17 (2016) 1-35, 2015

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:31:55.292739Z

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Observation 40196062-047e-4898-8d65-82e20fc7a5e3 · outbound

This paper cites Loss surfaces, mode connectivity, and fast ensembling of dnns.Advances in neural information processing systems, 31, 2018.

PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization Loss surfaces, mode connectivity, and fast ensembling of dnns.Advances in neural information processing systems, 31, 2018

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:31:55.278783Z

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Observation 80e3f74f-696a-4391-8eb1-062681ae1e6a · outbound

This paper cites Maybank, and Dacheng Tao.

PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization Maybank, and Dacheng Tao

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:31:55.264765Z

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Observation e11c2ba8-2546-44c1-bf3c-3ef586c0806a · outbound

This paper cites Bootstrap your own latent-a new approach to self-supervised learning.Advances in neural information processing systems, 33:21271–21284, 2020.

PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization Bootstrap your own latent-a new approach to self-supervised learning.Advances in neural information processing systems, 33:21271–21284, 2020

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:31:55.251133Z

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Observation e8865ef0-fd1f-49c5-9357-487626a13831 · outbound

This paper cites Understanding and improving the role of projection head in self-supervised learning, 2022.

PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization Understanding and improving the role of projection head in self-supervised learning, 2022

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:31:55.236907Z

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Observation 441965a0-4f60-492b-8843-d4394a183caf · outbound

This paper cites Distilling the knowledge in a neural network, 2015.

PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization Distilling the knowledge in a neural network, 2015

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:31:55.222689Z

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Observation a5d9fc3a-e2fe-4bfa-a0c6-3f8530de5d51 · outbound

This paper cites Learning deep representations by mutual information estimation and maximization.

PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization Learning deep representations by mutual information estimation and maximization

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:31:55.208189Z

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Observation d67afc7c-ed4e-4c27-bfc9-486af7e3154f · outbound

This paper cites Towards the generalization of contrastive self-supervised learning, 2021.

PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization Towards the generalization of contrastive self-supervised learning, 2021

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:31:55.193774Z

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Observation 722ecd45-dfdc-4971-8bab-140c46310d57 · outbound

This paper cites Averaging Weights Leads to Wider Optima and Better Generalization.

PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization Averaging Weights Leads to Wider Optima and Better Generalization

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-15T20:31:54.128133Z

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Observation 0de100b4-ee41-46b4-9f76-d96077d04381 · outbound

This paper cites PopulAtion Parameter Averaging (PAPA).

PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization PopulAtion Parameter Averaging (PAPA)

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-15T20:31:54.133109Z

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Observation 1f37a547-e43b-4c84-a1bf-f558ae7eb361 · outbound

This paper cites Kingma and Jimmy Ba.

PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization Kingma and Jimmy Ba

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:31:55.178767Z

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Observation 85409e0a-e911-4f0d-af72-6051329cdb49 · outbound

This paper cites Klindt, Lukas Schott, Yash Sharma, Ivan Ustyuzhaninov, Wieland Brendel, Matthias Bethge, and Dy- lan Paiton.

PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization Klindt, Lukas Schott, Yash Sharma, Ivan Ustyuzhaninov, Wieland Brendel, Matthias Bethge, and Dy- lan Paiton

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:31:55.162700Z

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Observation 7a88cd80-979f-4dc5-8a2f-a1f838d5b229 · outbound

This paper cites Springer Berlin Heidelberg, 2011.

PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization Springer Berlin Heidelberg, 2011

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:31:55.147353Z

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Observation df5df0c4-c8f5-498e-9403-a951e064df18 · outbound

This paper cites Similarity of neural network representations revisited.

PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization Similarity of neural network representations revisited

Reference 33

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Observation fbdb63ca-1d1c-488e-b584-b1befbea270d · outbound

This paper cites Im- agenet classification with deep convolutional neural networks.

PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization Im- agenet classification with deep convolutional neural networks

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:31:55.118290Z

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Observation 1266d7b8-6654-403c-9b92-062c8768da1f · outbound

This paper cites Fine-tuning can distort pre- trained features and underperform out-of-distribution.

PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization Fine-tuning can distort pre- trained features and underperform out-of-distribution

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:31:55.103397Z

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Observation ff4d4d83-dd15-4756-99bb-302e74d79381 · outbound

This paper cites Adver- sarial examples in the physical world.

PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization Adver- sarial examples in the physical world

Reference 36

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Observation 68130e5a-0519-41b4-8a4f-dc6b5724da4b · outbound

This paper cites Le Cun, B.

PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization Le Cun, B

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:31:55.077705Z

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Observation b1e6964e-23d1-4b00-9417-bde5f2196cde · outbound

This paper cites Deeper, broader and artier domain generaliza- tion.

PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization Deeper, broader and artier domain generaliza- tion

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:31:55.063744Z

Source-reported events for the cited work

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Observation d5378936-8713-46ea-910c-bf571034ee80 · outbound

This paper cites an unresolved cited work.

PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization Unresolved cited work

Reference 39

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:31:55.049253Z

Source-reported events for the cited work

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Observation 9b587e58-4c00-4a06-b460-90310e1189b4 · outbound

This paper cites SIMPLE: Specialized model- sample matching for domain generalization.

PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization SIMPLE: Specialized model- sample matching for domain generalization

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:31:55.033864Z

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 6e0d5407-5610-4c56-8306-ece873350905 · outbound

This paper cites Mechanistic mode connectiv- ity.

PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization Mechanistic mode connectiv- ity

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:31:55.018920Z

Source-reported events for the cited work

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Observation ba898e27-e9c8-48ec-9ef8-062ecba80af1 · outbound

This paper cites Weighted Ensemble Models Are Strong Continual Learners.

PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization Weighted Ensemble Models Are Strong Continual Learners

Reference 42

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Observation 34f6903d-1b1c-4140-9c6d-6da34a278575 · outbound

This paper cites an unresolved cited work.

PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization Unresolved cited work

Reference 43

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Observation 0bdc1740-890e-4914-98c8-385bac60d3a1 · outbound

This paper cites What is being transferred in transfer learning?Advances in neural information processing systems, 33:512–523, 2020.

PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization What is being transferred in transfer learning?Advances in neural information processing systems, 33:512–523, 2020

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:31:54.993496Z

Source-reported events for the cited work

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Observation 97395f9d-73ea-4983-8fbc-74d0f863ba6d · outbound

This paper cites Represen- tation learning with contrastive predictive coding, 2018.

PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization Represen- tation learning with contrastive predictive coding, 2018

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:31:54.978910Z

Source-reported events for the cited work

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Observation 3087b260-b067-4623-970e-5734d00d89a0 · outbound

This paper cites Estimation of entropy and mutual information.

PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization Estimation of entropy and mutual information

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:31:54.964152Z

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 5b5bb2b4-8516-4b50-b95d-1a0d5847a4a8 · outbound

This paper cites On variational bounds of mutual infor- mation.

PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization On variational bounds of mutual infor- mation

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:31:54.949195Z

Source-reported events for the cited work

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Observation 87b08db7-9cb6-4b6a-add1-46cc7dceb822 · outbound

This paper cites Learning to learn single domain generalization.

PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization Learning to learn single domain generalization

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:31:54.933389Z

Source-reported events for the cited work

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Observation 2f2c6941-d199-4492-8906-0adcc6d8fade · outbound

This paper cites Designing network design spaces.

PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization Designing network design spaces

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:31:54.918628Z

Source-reported events for the cited work

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Observation 5ad52b2c-c7da-41f0-a247-229c928e1ac1 · outbound

This paper cites Diverse weight averaging for out-of-distribution generaliza- tion.

PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization Diverse weight averaging for out-of-distribution generaliza- tion

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:31:54.904593Z

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Observation 28a7e7eb-09df-4b53-a30b-b3b8d272990f · outbound

This paper cites Model ratatouille: Re- cycling diverse models for out-of-distribution generalization.

PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization Model ratatouille: Re- cycling diverse models for out-of-distribution generalization

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:31:54.890030Z

Source-reported events for the cited work

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Observation b9f900b2-24d7-4508-a0c1-82724b05f4bb · outbound

This paper cites Rethinking content and style: Exploring bias for unsuper- vised disentanglement.

PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization Rethinking content and style: Exploring bias for unsuper- vised disentanglement

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:31:54.875365Z

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

Observation 86980ccc-5aa6-4214-8a6a-c1358a90c298 · outbound

This paper cites Berg, and Li Fei-Fei.

PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization Berg, and Li Fei-Fei

Reference 53

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This paper cites A unified approach to domain incremental learning with memory: Theory and algorithm.

PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization A unified approach to domain incremental learning with memory: Theory and algorithm

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:31:54.850742Z

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This paper cites Esti- mating and maximizing mutual information for knowledge distillation.

PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization Esti- mating and maximizing mutual information for knowledge distillation

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:31:54.836708Z

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

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PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization Unresolved cited work

Reference 56

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:31:54.823012Z

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Observation c8b6205d-7ca0-4466-9aff-f7d094968eab · outbound

This paper cites A note on connecting barlow twins with negative-sample-free contrastive learning, 2021.

PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization A note on connecting barlow twins with negative-sample-free contrastive learning, 2021

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:31:54.808982Z

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 bbd603ec-5a8a-4f47-b04d-8f424836222e · outbound

This paper cites Deep hashing network for unsupervised domain adaptation.

PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization Deep hashing network for unsupervised domain adaptation

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:31:54.794521Z

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 b4a518cb-1eea-4d40-9aed-a6dd6ee4fe62 · outbound

This paper cites Generalizing to unseen domains via adversarial data augmentation, 2018.

PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization Generalizing to unseen domains via adversarial data augmentation, 2018

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:31:54.780074Z

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

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This paper cites Generalizing to unseen domains via adversarial data augmentation.Advances in neural information processing systems, 31, 2018.

PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization Generalizing to unseen domains via adversarial data augmentation.Advances in neural information processing systems, 31, 2018

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:31:54.765550Z

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 21bb2794-48fa-4067-8982-a8d3ce6a7751 · outbound

This paper cites Self-supervised learning with data aug- mentations provably isolates content from style.Advances in neural information processing systems, 34:16451–16467,.

PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization Self-supervised learning with data aug- mentations provably isolates content from style.Advances in neural information processing systems, 34:16451–16467,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:31:54.751246Z

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 fb5e6692-c11e-4618-a582-6015f38e65f6 · outbound

This paper cites Meta convolutional neural networks for single domain generalization.

PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization Meta convolutional neural networks for single domain generalization

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:31:54.736609Z

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 fb911c60-1459-4cb1-8917-5620b271a7d6 · outbound

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PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization Unresolved cited work

Reference 63

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:31:54.722022Z

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 48d30c12-fc69-442e-a8c6-b53b6af38b39 · outbound

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

PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization A comprehensive survey of continual learning: Theory, method and application.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:31:54.707178Z

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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This paper cites Learning to diversify for single domain generalization.

PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization Learning to diversify for single domain generalization

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:31:54.690388Z

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 ee742c21-b7a6-4951-a668-f68654578501 · outbound

This paper cites Wolpert and W.G.

PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization Wolpert and W.G

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:31:54.675200Z

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 b1fb2688-ef4b-4b74-ae64-be24be15fcd8 · outbound

This paper cites Morcos, Hongseok Namkoong, Ali Farhadi, Yair Carmon, Simon Kornblith, and Ludwig Schmidt.

PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization Morcos, Hongseok Namkoong, Ali Farhadi, Yair Carmon, Simon Kornblith, and Ludwig Schmidt

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:31:54.660632Z

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 651cf350-1940-4462-aae7-245c7e1b4160 · outbound

This paper cites Simde: A simple domain expansion approach for single-source domain generalization.

PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization Simde: A simple domain expansion approach for single-source domain generalization

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:31:54.645819Z

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 369ff001-20e7-453e-92d3-c01750c1eb02 · outbound

This paper cites Barlow twins: Self-supervised learning via redundancy reduction.

PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization Barlow twins: Self-supervised learning via redundancy reduction

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:31:54.630968Z

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 5ec7ba2f-d00b-48d9-b948-0a4ff8fbae61 · outbound

This paper cites Can parameter-averaging proxy model snapshots without regular- ization create a robust regulator?.

PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization Can parameter-averaging proxy model snapshots without regular- ization create a robust regulator?

Reference 70

Resolution: malformed identifier; raw_fallback, observed 2026-08-15T20:31:54.503332Z

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 80297ad0-6279-4fd8-907a-bce13b89f91f · outbound

This paper cites Y" indicates the convolution method, and the.

PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization Y" indicates the convolution method, and the

Reference 71

Resolution: verified exact; raw_fallback, observed 2026-08-15T20:31:54.423194Z

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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