Improving Out-of-Domain Robustness with Targeted Augmentation in Frequency and Pixel Spaces | Paper Citation Record

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This paper cites an unresolved cited work.

Improving Out-of-Domain Robustness with Targeted Augmentation in Frequency and Pixel Spaces Unresolved cited work

Reference 1

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This paper cites The iWildCam 2021 Competition Dataset.

Improving Out-of-Domain Robustness with Targeted Augmentation in Frequency and Pixel Spaces The iWildCam 2021 Competition Dataset

Reference 2

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This paper cites Recognition in terra incognita.

Improving Out-of-Domain Robustness with Targeted Augmentation in Frequency and Pixel Spaces Recognition in terra incognita

Reference 3

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This paper cites Unsupervised learning of visual features by contrasting cluster assignments.Advances in neural information processing systems, 33:9912–9924, 2020.

Improving Out-of-Domain Robustness with Targeted Augmentation in Frequency and Pixel Spaces Unsupervised learning of visual features by contrasting cluster assignments.Advances in neural information processing systems, 33:9912–9924, 2020

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

Improving Out-of-Domain Robustness with Targeted Augmentation in Frequency and Pixel Spaces A simple framework for contrastive learning of visual representations

Reference 5

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

Improving Out-of-Domain Robustness with Targeted Augmentation in Frequency and Pixel Spaces Randaugment: Practical automated data augmentation with a reduced search space

Reference 6

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

Improving Out-of-Domain Robustness with Targeted Augmentation in Frequency and Pixel Spaces Improved Regularization of Convolutional Neural Networks with Cutout

Reference 7

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This paper cites Domain-adversarial training of neural networks.

Improving Out-of-Domain Robustness with Targeted Augmentation in Frequency and Pixel Spaces Domain-adversarial training of neural networks

Reference 8

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This paper cites Out-of-domain robustness via targeted augmentations.

Improving Out-of-Domain Robustness with Targeted Augmentation in Frequency and Pixel Spaces Out-of-domain robustness via targeted augmentations

Reference 9

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Observation 1ebd3975-9c8e-4a9d-a57a-d5ed12f29857 · outbound

This paper cites In search of lost domain generalization.

Improving Out-of-Domain Robustness with Targeted Augmentation in Frequency and Pixel Spaces In search of lost domain generalization

Reference 10

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This paper cites Structural sparseness and spatial phase alignment in natural scenes.Journal of the Optical Society of America A, 24(7):1873–1885, 2007.

Improving Out-of-Domain Robustness with Targeted Augmentation in Frequency and Pixel Spaces Structural sparseness and spatial phase alignment in natural scenes.Journal of the Optical Society of America A, 24(7):1873–1885, 2007

Reference 11

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This paper cites Deep residual learning for image recognition.

Improving Out-of-Domain Robustness with Targeted Augmentation in Frequency and Pixel Spaces Deep residual learning for image recognition

Reference 12

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

Improving Out-of-Domain Robustness with Targeted Augmentation in Frequency and Pixel Spaces The many faces of robustness: A critical analysis of out-of-distribution generalization

Reference 13

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

Improving Out-of-Domain Robustness with Targeted Augmentation in Frequency and Pixel Spaces Augmix: A simple data processing method to improve robustness and uncertainty

Reference 14

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This paper cites Galaxy10 decals dataset.

Improving Out-of-Domain Robustness with Targeted Augmentation in Frequency and Pixel Spaces Galaxy10 decals dataset

Reference 15

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This paper cites Cycada: Cycle-consistent adversarial domain adaptation.

Improving Out-of-Domain Robustness with Targeted Augmentation in Frequency and Pixel Spaces Cycada: Cycle-consistent adversarial domain adaptation

Reference 16

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This paper cites Cycada: Cycle-consistent adversarial domain adaptation.

Improving Out-of-Domain Robustness with Targeted Augmentation in Frequency and Pixel Spaces Cycada: Cycle-consistent adversarial domain adaptation

Reference 17

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This paper cites Selecting data augmentation for simulating interventions.

Improving Out-of-Domain Robustness with Targeted Augmentation in Frequency and Pixel Spaces Selecting data augmentation for simulating interventions

Reference 18

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This paper cites Overview of lifeclef 2022: an evaluation of machine-learning based species identification and species distribution prediction.

Improving Out-of-Domain Robustness with Targeted Augmentation in Frequency and Pixel Spaces Overview of lifeclef 2022: an evaluation of machine-learning based species identification and species distribution prediction

Reference 19

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Observation 4ddc8fc1-e1f3-4176-80fa-a48283f5a075 · outbound

This paper cites Contrastive adaptation network for unsupervised domain adaptation.

Improving Out-of-Domain Robustness with Targeted Augmentation in Frequency and Pixel Spaces Contrastive adaptation network for unsupervised domain adaptation

Reference 20

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This paper cites Wilds: A benchmark of in-the-wild distribution shifts.

Improving Out-of-Domain Robustness with Targeted Augmentation in Frequency and Pixel Spaces Wilds: A benchmark of in-the-wild distribution shifts

Reference 21

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Observation 43c1a4e5-c48c-46a0-a616-57e1c227edcb · outbound

This paper cites Imagenet classification with deep convolutional neural networks.Advances in neural information processing systems, 25, 2012.

Improving Out-of-Domain Robustness with Targeted Augmentation in Frequency and Pixel Spaces Imagenet classification with deep convolutional neural networks.Advances in neural information processing systems, 25, 2012

Reference 22

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

Improving Out-of-Domain Robustness with Targeted Augmentation in Frequency and Pixel Spaces Fine-tuning can distort pretrained features and underperform out-of-distribution

Reference 23

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This paper cites Conditional adversarial domain adaptation.Advances in neural information processing systems, 31, 2018.

Improving Out-of-Domain Robustness with Targeted Augmentation in Frequency and Pixel Spaces Conditional adversarial domain adaptation.Advances in neural information processing systems, 31, 2018

Reference 24

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This paper cites SSMBA: Self-Supervised Manifold Based Data Augmentation for Improving Out-of-Domain Robustness.

Improving Out-of-Domain Robustness with Targeted Augmentation in Frequency and Pixel Spaces SSMBA: Self-Supervised Manifold Based Data Augmentation for Improving Out-of-Domain Robustness

Reference 25

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This paper cites Phase in speech and pictures.

Improving Out-of-Domain Robustness with Targeted Augmentation in Frequency and Pixel Spaces Phase in speech and pictures

Reference 26

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This paper cites The importance of phase in signals.Proceedings of the IEEE, 69(5):529–541, 1981.

Improving Out-of-Domain Robustness with Targeted Augmentation in Frequency and Pixel Spaces The importance of phase in signals.Proceedings of the IEEE, 69(5):529–541, 1981

Reference 27

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This paper cites SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition.

Improving Out-of-Domain Robustness with Targeted Augmentation in Frequency and Pixel Spaces SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition

Reference 28

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This paper cites A demonstration of the visual importance and flexibility of spatial-frequency amplitude and phase.Perception, 11(3):337–346, 1982.

Improving Out-of-Domain Robustness with Targeted Augmentation in Frequency and Pixel Spaces A demonstration of the visual importance and flexibility of spatial-frequency amplitude and phase.Perception, 11(3):337–346, 1982

Reference 29

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This paper cites Connect later: improving fine-tuning for robustness with targeted augmentations.

Improving Out-of-Domain Robustness with Targeted Augmentation in Frequency and Pixel Spaces Connect later: improving fine-tuning for robustness with targeted augmentations

Reference 30

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

Improving Out-of-Domain Robustness with Targeted Augmentation in Frequency and Pixel Spaces Learning transferable visual models from natural language supervision

Reference 31

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This paper cites Data augmentation can improve robustness.Advances in neural information processing systems, 34:29935–29948, 2021.

Improving Out-of-Domain Robustness with Targeted Augmentation in Frequency and Pixel Spaces Data augmentation can improve robustness.Advances in neural information processing systems, 34:29935–29948, 2021

Reference 32

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This paper cites Extending the wilds bench- mark for unsupervised adaptation.

Improving Out-of-Domain Robustness with Targeted Augmentation in Frequency and Pixel Spaces Extending the wilds bench- mark for unsupervised adaptation

Reference 33

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This paper cites Noisereduce: Domain General Noise Reduction for Time Series Signals.

Improving Out-of-Domain Robustness with Targeted Augmentation in Frequency and Pixel Spaces Noisereduce: Domain General Noise Reduction for Time Series Signals

Reference 34

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This paper cites Demystifying galaxy classification: An elegant and powerful hybrid approach.

Improving Out-of-Domain Robustness with Targeted Augmentation in Frequency and Pixel Spaces Demystifying galaxy classification: An elegant and powerful hybrid approach

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This paper cites Connect, not collapse: Explaining contrastive learning for unsupervised domain adaptation.

Improving Out-of-Domain Robustness with Targeted Augmentation in Frequency and Pixel Spaces Connect, not collapse: Explaining contrastive learning for unsupervised domain adaptation

Reference 36

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This paper cites Best practices for convolutional neural networks applied to visual document analysis.

Improving Out-of-Domain Robustness with Targeted Augmentation in Frequency and Pixel Spaces Best practices for convolutional neural networks applied to visual document analysis

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This paper cites Fixmatch: Simplifying semi- supervised learning with consistency and confidence.Advances in neural information processing systems, 33:596–608, 2020.

Improving Out-of-Domain Robustness with Targeted Augmentation in Frequency and Pixel Spaces Fixmatch: Simplifying semi- supervised learning with consistency and confidence.Advances in neural information processing systems, 33:596–608, 2020

Reference 38

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This paper cites Correlation alignment for unsupervised domain adaptation.Domain adaptation in computer vision applications, pages 153–171, 2017.

Improving Out-of-Domain Robustness with Targeted Augmentation in Frequency and Pixel Spaces Correlation alignment for unsupervised domain adaptation.Domain adaptation in computer vision applications, pages 153–171, 2017

Reference 39

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Improving Out-of-Domain Robustness with Targeted Augmentation in Frequency and Pixel Spaces Deep coral: Correlation alignment for deep domain adaptation

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Improving Out-of-Domain Robustness with Targeted Augmentation in Frequency and Pixel Spaces Unresolved cited work

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This paper cites Galaxy morphology classification with densenet.

Improving Out-of-Domain Robustness with Targeted Augmentation in Frequency and Pixel Spaces Galaxy morphology classification with densenet

Reference 42

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This paper cites A fine-grained analysis on distribution shift.

Improving Out-of-Domain Robustness with Targeted Augmentation in Frequency and Pixel Spaces A fine-grained analysis on distribution shift

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This paper cites Unsupervised data augmenta- tion for consistency training.Advances in neural information processing systems, 33:6256–6268, 2020.

Improving Out-of-Domain Robustness with Targeted Augmentation in Frequency and Pixel Spaces Unsupervised data augmenta- tion for consistency training.Advances in neural information processing systems, 33:6256–6268, 2020

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This paper cites Self-training with noisy student improves imagenet classification.

Improving Out-of-Domain Robustness with Targeted Augmentation in Frequency and Pixel Spaces Self-training with noisy student improves imagenet classification

Reference 45

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This paper cites Fourier-based augmentation with applications to domain generalization.Pattern Recognition, 139:109474, 2023.

Improving Out-of-Domain Robustness with Targeted Augmentation in Frequency and Pixel Spaces Fourier-based augmentation with applications to domain generalization.Pattern Recognition, 139:109474, 2023

Reference 46

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This paper cites Improve Unsupervised Domain Adaptation with Mixup Training.

Improving Out-of-Domain Robustness with Targeted Augmentation in Frequency and Pixel Spaces Improve Unsupervised Domain Adaptation with Mixup Training

Reference 47

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This paper cites Interactive self-training with mean teachers for semi-supervised object detection.

Improving Out-of-Domain Robustness with Targeted Augmentation in Frequency and Pixel Spaces Interactive self-training with mean teachers for semi-supervised object detection

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Observation 79b3cf18-86fa-43d4-a89a-35d291d6b2de · outbound

This paper cites Phase consistent ecological domain adaptation.

Improving Out-of-Domain Robustness with Targeted Augmentation in Frequency and Pixel Spaces Phase consistent ecological domain adaptation

Reference 49

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Observation 80a70567-5d7c-44c1-988b-d03297ab7cf6 · outbound

This paper cites Fda: Fourier domain adaptation for semantic segmentation.

Improving Out-of-Domain Robustness with Targeted Augmentation in Frequency and Pixel Spaces Fda: Fourier domain adaptation for semantic segmentation

Reference 50

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This paper cites Im- proving out-of-distribution robustness via selective augmentation.

Improving Out-of-Domain Robustness with Targeted Augmentation in Frequency and Pixel Spaces Im- proving out-of-distribution robustness via selective augmentation

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Observation c1f0b18d-b93f-4a93-92af-9f4427446292 · outbound

This paper cites Cutmix: Regularization strategy to train strong classifiers with localizable features.

Improving Out-of-Domain Robustness with Targeted Augmentation in Frequency and Pixel Spaces Cutmix: Regularization strategy to train strong classifiers with localizable features

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Observation 51bc31fc-a2d0-4f33-8152-804426000917 · outbound

This paper cites mixup: Beyond Empirical Risk Minimization.

Improving Out-of-Domain Robustness with Targeted Augmentation in Frequency and Pixel Spaces mixup: Beyond Empirical Risk Minimization

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-15T20:41:31.858280Z

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Observation 9830623a-d5fd-4ff9-bd6b-f51d2b0075ed · outbound

This paper cites Deep domain-adversarial image generation for domain generalisation.

Improving Out-of-Domain Robustness with Targeted Augmentation in Frequency and Pixel Spaces Deep domain-adversarial image generation for domain generalisation

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:41:32.039683Z

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Observation 3bf7920f-6bfa-480c-b01a-6b3e0e624504 · outbound

This paper cites This underscores the importance of pretrained representations in achieving robustness gains.

Improving Out-of-Domain Robustness with Targeted Augmentation in Frequency and Pixel Spaces This underscores the importance of pretrained representations in achieving robustness gains

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:41:32.026951Z

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Observation 4fd4ff25-98db-4d34-9380-895f21b85b19 · outbound

This paper cites Xu et al.

Improving Out-of-Domain Robustness with Targeted Augmentation in Frequency and Pixel Spaces Xu et al

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:41:32.013422Z

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