Feed Two Birds with One Scone: Exploiting Function-Space Regularization for Both OOD Robustness and ID Fine-Tuning Performance | Paper Citation Record

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This paper cites Invariance principle meets information bottleneck for out-of-distribution generalization.

Feed Two Birds with One Scone: Exploiting Function-Space Regularization for Both OOD Robustness and ID Fine-Tuning Performance Invariance principle meets information bottleneck for out-of-distribution generalization

Reference 1

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This paper cites Objectnet: A large-scale bias-controlled dataset for pushing the limits of object recognition models.

Feed Two Birds with One Scone: Exploiting Function-Space Regularization for Both OOD Robustness and ID Fine-Tuning Performance Objectnet: A large-scale bias-controlled dataset for pushing the limits of object recognition models

Reference 2

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This paper cites Measuring and regularizing networks in function space.

Feed Two Birds with One Scone: Exploiting Function-Space Regularization for Both OOD Robustness and ID Fine-Tuning Performance Measuring and regularizing networks in function space

Reference 3

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This paper cites Benjamin, David Rolnick, and Konrad P.

Feed Two Birds with One Scone: Exploiting Function-Space Regularization for Both OOD Robustness and ID Fine-Tuning Performance Benjamin, David Rolnick, and Konrad P

Reference 4

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This paper cites A kernel perspective for regularizing deep neural networks.

Feed Two Birds with One Scone: Exploiting Function-Space Regularization for Both OOD Robustness and ID Fine-Tuning Performance A kernel perspective for regularizing deep neural networks

Reference 5

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This paper cites On the Opportunities and Risks of Foundation Models.

Feed Two Birds with One Scone: Exploiting Function-Space Regularization for Both OOD Robustness and ID Fine-Tuning Performance On the Opportunities and Risks of Foundation Models

Reference 6

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This paper cites Language models are few-shot learners.

Feed Two Birds with One Scone: Exploiting Function-Space Regularization for Both OOD Robustness and ID Fine-Tuning Performance Language models are few-shot learners

Reference 7

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This paper cites Burt, Sebastian W.

Feed Two Birds with One Scone: Exploiting Function-Space Regularization for Both OOD Robustness and ID Fine-Tuning Performance Burt, Sebastian W

Reference 8

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This paper cites Multi-dimensional graph linear canonical transform and its application.

Feed Two Birds with One Scone: Exploiting Function-Space Regularization for Both OOD Robustness and ID Fine-Tuning Performance Multi-dimensional graph linear canonical transform and its application

Reference 9

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Feed Two Birds with One Scone: Exploiting Function-Space Regularization for Both OOD Robustness and ID Fine-Tuning Performance Unresolved cited work

Reference 10

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

Feed Two Birds with One Scone: Exploiting Function-Space Regularization for Both OOD Robustness and ID Fine-Tuning Performance Randaugment: Practical automated data augmentation with a reduced search space

Reference 11

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This paper cites Improved Regularization of Convolutional Neural Networks with Cutout.

Feed Two Birds with One Scone: Exploiting Function-Space Regularization for Both OOD Robustness and ID Fine-Tuning Performance Improved Regularization of Convolutional Neural Networks with Cutout

Reference 12

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This paper cites Sharpness-Aware Minimization for Efficiently Improving Generalization.

Feed Two Birds with One Scone: Exploiting Function-Space Regularization for Both OOD Robustness and ID Fine-Tuning Performance Sharpness-Aware Minimization for Efficiently Improving Generalization

Reference 13

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This paper cites Domain-adversarial training of neural networks.

Feed Two Birds with One Scone: Exploiting Function-Space Regularization for Both OOD Robustness and ID Fine-Tuning Performance Domain-adversarial training of neural networks

Reference 14

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This paper cites Explaining and Harnessing Adversarial Examples.

Feed Two Birds with One Scone: Exploiting Function-Space Regularization for Both OOD Robustness and ID Fine-Tuning Performance Explaining and Harnessing Adversarial Examples

Reference 15

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This paper cites Finetune like you pretrain: Improved finetuning of zero-shot vision models.

Feed Two Birds with One Scone: Exploiting Function-Space Regularization for Both OOD Robustness and ID Fine-Tuning Performance Finetune like you pretrain: Improved finetuning of zero-shot vision models

Reference 16

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This paper cites Natural adversarial examples.

Feed Two Birds with One Scone: Exploiting Function-Space Regularization for Both OOD Robustness and ID Fine-Tuning Performance Natural adversarial examples

Reference 17

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This paper cites Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwi ´n´ska, et al.

Feed Two Birds with One Scone: Exploiting Function-Space Regularization for Both OOD Robustness and ID Fine-Tuning Performance Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwi ´n´ska, et al

Reference 18

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

Feed Two Birds with One Scone: Exploiting Function-Space Regularization for Both OOD Robustness and ID Fine-Tuning Performance Fine- tuning can distort pretrained features and underperform out-of-distribution

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:19:04.018772Z

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This paper cites Invariant risk minimization is a total variation model.

Feed Two Birds with One Scone: Exploiting Function-Space Regularization for Both OOD Robustness and ID Fine-Tuning Performance Invariant risk minimization is a total variation model

Reference 20

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This paper cites Temporal ensembling for semi-supervised learning.

Feed Two Birds with One Scone: Exploiting Function-Space Regularization for Both OOD Robustness and ID Fine-Tuning Performance Temporal ensembling for semi-supervised learning

Reference 21

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This paper cites Surgical Fine-Tuning Improves Adaptation to Distribution Shifts.

Feed Two Birds with One Scone: Exploiting Function-Space Regularization for Both OOD Robustness and ID Fine-Tuning Performance Surgical Fine-Tuning Improves Adaptation to Distribution Shifts

Reference 22

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Observation 395b9e7a-3289-4ee9-8ebb-4c81ad207dd9 · outbound

This paper cites Towards Out-Of-Distribution Generalization: A Survey.

Feed Two Birds with One Scone: Exploiting Function-Space Regularization for Both OOD Robustness and ID Fine-Tuning Performance Towards Out-Of-Distribution Generalization: A Survey

Reference 23

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This paper cites Context-aware robust fine-tuning.

Feed Two Birds with One Scone: Exploiting Function-Space Regularization for Both OOD Robustness and ID Fine-Tuning Performance Context-aware robust fine-tuning

Reference 24

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This paper cites Virtual adversarial training: a regularization method for supervised and semi-supervised learning.

Feed Two Birds with One Scone: Exploiting Function-Space Regularization for Both OOD Robustness and ID Fine-Tuning Performance Virtual adversarial training: a regularization method for supervised and semi-supervised learning

Reference 25

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This paper cites Fine-tuning can cripple your foundation model; preserving features may be the solution.

Feed Two Birds with One Scone: Exploiting Function-Space Regularization for Both OOD Robustness and ID Fine-Tuning Performance Fine-tuning can cripple your foundation model; preserving features may be the solution

Reference 26

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This paper cites Lipsum-ft: Robust fine-tuning of zero-shot models using random text guidance.

Feed Two Birds with One Scone: Exploiting Function-Space Regularization for Both OOD Robustness and ID Fine-Tuning Performance Lipsum-ft: Robust fine-tuning of zero-shot models using random text guidance

Reference 27

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This paper cites Dawin: Training- free dynamic weight interpolation for robust adaptation, 2024.

Feed Two Birds with One Scone: Exploiting Function-Space Regularization for Both OOD Robustness and ID Fine-Tuning Performance Dawin: Training- free dynamic weight interpolation for robust adaptation, 2024

Reference 28

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This paper cites Towards calibrated robust fine-tuning of vision-language models.

Feed Two Birds with One Scone: Exploiting Function-Space Regularization for Both OOD Robustness and ID Fine-Tuning Performance Towards calibrated robust fine-tuning of vision-language models

Reference 29

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

Feed Two Birds with One Scone: Exploiting Function-Space Regularization for Both OOD Robustness and ID Fine-Tuning Performance Learning transferable visual models from natural language supervision

Reference 30

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Feed Two Birds with One Scone: Exploiting Function-Space Regularization for Both OOD Robustness and ID Fine-Tuning Performance Unresolved cited work

Reference 31

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This paper cites Test-Time Training with Self-Supervision for Generalization under Distribution Shifts.

Feed Two Birds with One Scone: Exploiting Function-Space Regularization for Both OOD Robustness and ID Fine-Tuning Performance Test-Time Training with Self-Supervision for Generalization under Distribution Shifts

Reference 32

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This paper cites Distributionally robust neural networks for group shifts.

Feed Two Birds with One Scone: Exploiting Function-Space Regularization for Both OOD Robustness and ID Fine-Tuning Performance Distributionally robust neural networks for group shifts

Reference 33

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This paper cites A survey on image data augmentation for deep learning.

Feed Two Birds with One Scone: Exploiting Function-Space Regularization for Both OOD Robustness and ID Fine-Tuning Performance A survey on image data augmentation for deep learning

Reference 34

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Feed Two Birds with One Scone: Exploiting Function-Space Regularization for Both OOD Robustness and ID Fine-Tuning Performance Functional variational bayesian neural networks

Reference 35

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This paper cites Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results.

Feed Two Birds with One Scone: Exploiting Function-Space Regularization for Both OOD Robustness and ID Fine-Tuning Performance Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results

Reference 36

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This paper cites Trainable projected gradient method for robust fine-tuning.

Feed Two Birds with One Scone: Exploiting Function-Space Regularization for Both OOD Robustness and ID Fine-Tuning Performance Trainable projected gradient method for robust fine-tuning

Reference 37

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Feed Two Birds with One Scone: Exploiting Function-Space Regularization for Both OOD Robustness and ID Fine-Tuning Performance Fast trainable projection for robust fine-tuning

Reference 38

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This paper cites Titsias, Jonathan Schwarz, Alexander de G.

Feed Two Birds with One Scone: Exploiting Function-Space Regularization for Both OOD Robustness and ID Fine-Tuning Performance Titsias, Jonathan Schwarz, Alexander de G

Reference 39

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This paper cites Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time.

Feed Two Birds with One Scone: Exploiting Function-Space Regularization for Both OOD Robustness and ID Fine-Tuning Performance Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time

Reference 40

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This paper cites Robust fine-tuning of zero-shot models.

Feed Two Birds with One Scone: Exploiting Function-Space Regularization for Both OOD Robustness and ID Fine-Tuning Performance Robust fine-tuning of zero-shot models

Reference 41

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This paper cites Unsupervised data augmentation for consistency training.

Feed Two Birds with One Scone: Exploiting Function-Space Regularization for Both OOD Robustness and ID Fine-Tuning Performance Unsupervised data augmentation for consistency training

Reference 42

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This paper cites Explicit inductive bias for transfer learning with convolutional networks.

Feed Two Birds with One Scone: Exploiting Function-Space Regularization for Both OOD Robustness and ID Fine-Tuning Performance Explicit inductive bias for transfer learning with convolutional networks

Reference 43

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This paper cites Sample efficiency of data augmentation consistency regularization.

Feed Two Birds with One Scone: Exploiting Function-Space Regularization for Both OOD Robustness and ID Fine-Tuning Performance Sample efficiency of data augmentation consistency regularization

Reference 44

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This paper cites Learning to generate novel domains for domain generalization.

Feed Two Birds with One Scone: Exploiting Function-Space Regularization for Both OOD Robustness and ID Fine-Tuning Performance Learning to generate novel domains for domain generalization

Reference 45

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This paper cites Domain generalization with mixstyle.

Feed Two Birds with One Scone: Exploiting Function-Space Regularization for Both OOD Robustness and ID Fine-Tuning Performance Domain generalization with mixstyle

Reference 46

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