TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction | Paper Citation Record

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This paper cites A-star: Test-time attention segregation and retention for text-to-image synthesis.

TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction A-star: Test-time attention segregation and retention for text-to-image synthesis

Reference 1

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This paper cites Language models are few-shot learners.

TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction Language models are few-shot learners

Reference 2

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This paper cites Attend-and-excite: Attention-based se- mantic guidance for text-to-image diffusion models.

TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction Attend-and-excite: Attention-based se- mantic guidance for text-to-image diffusion models

Reference 3

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This paper cites Meta-causal learning for single domain generalization.

TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction Meta-causal learning for single domain generalization

Reference 4

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This paper cites Adversar- ial bayesian augmentation for single-source domain gener- alization.

TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction Adversar- ial bayesian augmentation for single-source domain gener- alization

Reference 5

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This paper cites Progressive random con- volutions for single domain generalization.

TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction Progressive random con- volutions for single domain generalization

Reference 6

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

TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction Randaugment: Practical automated data augmentation with a reduced search space

Reference 7

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This paper cites Attention consistency on visual corruptions for single-source domain generalization.

TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction Attention consistency on visual corruptions for single-source domain generalization

Reference 8

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

TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction Improved Regularization of Convolutional Neural Networks with Cutout

Reference 9

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This paper cites Learning to learn with variational information bottleneck for domain gener- alization.

TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction Learning to learn with variational information bottleneck for domain gener- alization

Reference 10

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This paper cites Domain gener- alization with domain-specific aggregation modules.

TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction Domain gener- alization with domain-specific aggregation modules

Reference 11

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This paper cites Adversarially adaptive normalization for single domain generalization.

TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction Adversarially adaptive normalization for single domain generalization

Reference 12

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

TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction Unbiased met- ric learning: On the utilization of multiple datasets and web images for softening bias

Reference 13

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This paper cites Imagenet-trained cnns are biased towards texture; increas- ing shape bias improves accuracy and robustness.

TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction Imagenet-trained cnns are biased towards texture; increas- ing shape bias improves accuracy and robustness

Reference 14

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This paper cites Domain generalization for object recog- nition with multi-task autoencoders.

TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction Domain generalization for object recog- nition with multi-task autoencoders

Reference 15

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This paper cites Attribute-guided adversarial training for robustness to nat- ural perturbations.

TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction Attribute-guided adversarial training for robustness to nat- ural perturbations

Reference 16

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This paper cites In search of lost do- main generalization.

TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction In search of lost do- main generalization

Reference 17

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This paper cites Deep residual learning for image recognition.

TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction Deep residual learning for image recognition

Reference 18

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This paper cites Augmix: A simple data processing method to improve robustness and uncertainty.

TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction Augmix: A simple data processing method to improve robustness and uncertainty

Reference 19

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TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction Pixmix: Dream- like pictures comprehensively improve safety measures

Reference 20

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This paper cites Self-challenging improves cross-domain generalization.

TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction Self-challenging improves cross-domain generalization

Reference 21

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TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction Spatial transformer networks

Reference 22

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TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction Concept bottleneck models

Reference 23

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This paper cites A review of domain adap- tation without target labels.

TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction A review of domain adap- tation without target labels

Reference 24

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TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction Imagenet classification with deep convolutional neural net- works

Reference 25

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This paper cites Deeper, broader and artier domain general- ization.

TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction Deeper, broader and artier domain general- ization

Reference 26

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This paper cites Prompt-driven dynamic object-centric learning for single do- main generalization.

TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction Prompt-driven dynamic object-centric learning for single do- main generalization

Reference 27

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TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction Uncertainty modeling for out- of-distribution generalization

Reference 28

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TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction Deep domain gener- alization via conditional invariant adversarial networks

Reference 29

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TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction Towards Out-Of-Distribution Generalization: A Survey

Reference 30

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TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction Learning transferable features with deep adaptation net- works

Reference 31

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This paper cites Do concept bot- tleneck models learn as intended? International Conference on Learning Representations (ICLR), 2021.

TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction Do concept bot- tleneck models learn as intended? International Conference on Learning Representations (ICLR), 2021

Reference 32

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TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction Unified deep supervised domain adap- tation and generalization

Reference 33

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TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction Domain generalization via invariant fea- ture representation

Reference 34

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TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction Permuted adain: Reducing the bias towards global statistics in image clas- sification

Reference 35

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TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction Label-free concept bottleneck models

Reference 36

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TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction Moment matching for multi-source domain adaptation

Reference 37

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TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction Generalizing to unseen domains via text-guided augmenta- tion

Reference 38

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TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction Learning to learn single domain generalization

Reference 39

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TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction Modality-agnostic debiasing for single domain generalization

Reference 40

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TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction High-resolution image syn- thesis with latent diffusion models

Reference 41

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TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction Grad-cam: Visual explanations from deep networks via gradient-based localization

Reference 42

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This paper cites Class-wise domain generalization: A novel framework for evaluating distributional shift.

TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction Class-wise domain generalization: A novel framework for evaluating distributional shift

Reference 43

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TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction Frustratingly Simple Domain Generalization via Image Stylization

Reference 44

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TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction Class- imbalanced domain adaptation: An empirical odyssey

Reference 45

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TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction Emergent correspondence from image diffusion

Reference 46

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TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction Unbiased look at dataset bias

Reference 47

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This paper cites Adversarial discriminative domain adaptation.

TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction Adversarial discriminative domain adaptation

Reference 48

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This paper cites Deep hashing network for unsupervised domain adaptation.

TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction Deep hashing network for unsupervised domain adaptation

Reference 49

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

TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction Generalizing to unseen domains via adversarial data augmentation

Reference 50

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This paper cites Mcpnet: An interpretable classifier via multi-level concept prototypes.

TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction Mcpnet: An interpretable classifier via multi-level concept prototypes

Reference 51

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This paper cites Learning robust representations by projecting super- ficial statistics out.

TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction Learning robust representations by projecting super- ficial statistics out

Reference 52

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This paper cites Generalizing to unseen domains: A survey on do- main generalization.

TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction Generalizing to unseen domains: A survey on do- main generalization

Reference 53

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TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction Learning to diversify for single do- main generalization

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:30:02.681784Z

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This paper cites A bit more bayesian: Domain-invariant learning with uncertainty.

TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction A bit more bayesian: Domain-invariant learning with uncertainty

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:30:02.666462Z

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

TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction Simde: A simple domain ex- pansion approach for single-source domain generalization

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:30:02.650747Z

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This paper cites Language in a bottle: Language model guided concept bottlenecks for in- terpretable image classification.

TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction Language in a bottle: Language model guided concept bottlenecks for in- terpretable image classification

Reference 57

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This paper cites Not just pretty pictures: Toward interventional data augmentation using text-to-image generators.

TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction Not just pretty pictures: Toward interventional data augmentation using text-to-image generators

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:30:02.618791Z

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This paper cites Cutmix: Regu- larization strategy to train strong classifiers with localizable features.

TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction Cutmix: Regu- larization strategy to train strong classifiers with localizable features

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:30:02.602430Z

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This paper cites Dauphin, and David Lopez-Paz.

TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction Dauphin, and David Lopez-Paz

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:30:02.585064Z

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Observation a3487d31-8eb8-4214-b794-84fa383a58c0 · outbound

This paper cites Towards principled disentanglement for domain generalization.

TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction Towards principled disentanglement for domain generalization

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:30:02.568882Z

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Observation 582822da-e72d-4990-bd7c-cf7978cc593c · outbound

This paper cites Maximum-entropy adversarial data augmentation for im- proved generalization and robustness.

TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction Maximum-entropy adversarial data augmentation for im- proved generalization and robustness

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:30:02.552674Z

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Observation ed4c394a-f7f1-4fdd-80e8-036145a40c3f · outbound

This paper cites Do- main generalization with mixstyle.

TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction Do- main generalization with mixstyle

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:30:02.536658Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 07bb6394-15d8-4b7f-a8de-c2df5c89b507 · outbound

This paper cites Mixstyle neural networks for domain generalization and adaptation.

TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction Mixstyle neural networks for domain generalization and adaptation

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:30:02.519441Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Outbound references

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