Detecting Regional Spurious Correlations in Vision Transformers via Token Discarding | Paper Citation Record

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This paper cites Sanity checks for saliency maps.

Detecting Regional Spurious Correlations in Vision Transformers via Token Discarding Sanity checks for saliency maps

Reference 1

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This paper cites Invariant Risk Minimization.

Detecting Regional Spurious Correlations in Vision Transformers via Token Discarding Invariant Risk Minimization

Reference 2

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This paper cites Towards improved input masking for convo- lutional neural networks.

Detecting Regional Spurious Correlations in Vision Transformers via Token Discarding Towards improved input masking for convo- lutional neural networks

Reference 3

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This paper cites Are we done with ImageNet?.

Detecting Regional Spurious Correlations in Vision Transformers via Token Discarding Are we done with ImageNet?

Reference 4

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This paper cites Token Merging: Your ViT But Faster.

Detecting Regional Spurious Correlations in Vision Transformers via Token Discarding Token Merging: Your ViT But Faster

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This paper cites Emerging properties in self-supervised vision transform- ers.

Detecting Regional Spurious Correlations in Vision Transformers via Token Discarding Emerging properties in self-supervised vision transform- ers

Reference 6

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This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Detecting Regional Spurious Correlations in Vision Transformers via Token Discarding An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 7

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This paper cites Shortcut Learning of Large Language Models in Natural Language Understanding.

Detecting Regional Spurious Correlations in Vision Transformers via Token Discarding Shortcut Learning of Large Language Models in Natural Language Understanding

Reference 8

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This paper cites Domain-adversarial training of neural networks.

Detecting Regional Spurious Correlations in Vision Transformers via Token Discarding Domain-adversarial training of neural networks

Reference 9

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Detecting Regional Spurious Correlations in Vision Transformers via Token Discarding Shortcut learning in deep neu- ral networks

Reference 10

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This paper cites Are vision transformers robust to spurious corre- lations? International Journal of Computer Vision, 132(3):689–709, 2024.

Detecting Regional Spurious Correlations in Vision Transformers via Token Discarding Are vision transformers robust to spurious corre- lations? International Journal of Computer Vision, 132(3):689–709, 2024

Reference 11

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This paper cites Which tokens to use? investigating token reduction in vision transformers.

Detecting Regional Spurious Correlations in Vision Transformers via Token Discarding Which tokens to use? investigating token reduction in vision transformers

Reference 12

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This paper cites Masked autoencoders are scalable vision learners.

Detecting Regional Spurious Correlations in Vision Transformers via Token Discarding Masked autoencoders are scalable vision learners

Reference 13

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This paper cites Deep residual learning for image recognition.

Detecting Regional Spurious Correlations in Vision Transformers via Token Discarding Deep residual learning for image recognition

Reference 14

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This paper cites Missingness Bias in Model Debugging.

Detecting Regional Spurious Correlations in Vision Transformers via Token Discarding Missingness Bias in Model Debugging

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This paper cites Feature Alignment and Restoration for Domain Generalization and Adaptation.

Detecting Regional Spurious Correlations in Vision Transformers via Token Discarding Feature Alignment and Restoration for Domain Generalization and Adaptation

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This paper cites Contrastive self- supervised learning: a survey on different architectures.

Detecting Regional Spurious Correlations in Vision Transformers via Token Discarding Contrastive self- supervised learning: a survey on different architectures

Reference 17

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This paper cites The (un) reliability of saliency methods.

Detecting Regional Spurious Correlations in Vision Transformers via Token Discarding The (un) reliability of saliency methods

Reference 18

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This paper cites Last Layer Re-Training is Sufficient for Robustness to Spurious Correlations.

Detecting Regional Spurious Correlations in Vision Transformers via Token Discarding Last Layer Re-Training is Sufficient for Robustness to Spurious Correlations

Reference 19

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This paper cites Gradient-based learning applied to document recognition.

Detecting Regional Spurious Correlations in Vision Transformers via Token Discarding Gradient-based learning applied to document recognition

Reference 20

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This paper cites Just train twice: Improving group robustness without training group information.

Detecting Regional Spurious Correlations in Vision Transformers via Token Discarding Just train twice: Improving group robustness without training group information

Reference 21

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This paper cites Deep learning face attributes in the wild.

Detecting Regional Spurious Correlations in Vision Transformers via Token Discarding Deep learning face attributes in the wild

Reference 22

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This paper cites Beyond attentive tokens: Incorporating token importance and diversity for efficient vision transformers.

Detecting Regional Spurious Correlations in Vision Transformers via Token Discarding Beyond attentive tokens: Incorporating token importance and diversity for efficient vision transformers

Reference 23

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Detecting Regional Spurious Correlations in Vision Transformers via Token Discarding Domain-invariant Feature Exploration for Domain Generalization

Reference 24

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This paper cites Faithfulness Measurable Masked Language Models.

Detecting Regional Spurious Correlations in Vision Transformers via Token Discarding Faithfulness Measurable Masked Language Models

Reference 25

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This paper cites Hard imagenet: Segmentations for objects with strong spurious cues.

Detecting Regional Spurious Correlations in Vision Transformers via Token Discarding Hard imagenet: Segmentations for objects with strong spurious cues

Reference 26

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Detecting Regional Spurious Correlations in Vision Transformers via Token Discarding Vindr-mammo: A large-scale benchmark dataset for computer-aided diagnosis in full- field digital mammography

Reference 27

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This paper cites Hidden stratification causes clinically meaningful fail- ures in machine learning for medical imaging.

Detecting Regional Spurious Correlations in Vision Transformers via Token Discarding Hidden stratification causes clinically meaningful fail- ures in machine learning for medical imaging

Reference 28

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This paper cites Selection of Source Images Heavily Influences the Effectiveness of Adversarial Attacks.

Detecting Regional Spurious Correlations in Vision Transformers via Token Discarding Selection of Source Images Heavily Influences the Effectiveness of Adversarial Attacks

Reference 29

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Detecting Regional Spurious Correlations in Vision Transformers via Token Discarding Unresolved cited work

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Detecting Regional Spurious Correlations in Vision Transformers via Token Discarding Know your self-supervised learning: A survey on image-based generative and discriminative training

Reference 31

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Detecting Regional Spurious Correlations in Vision Transformers via Token Discarding Evaluating Adversarial Attacks on ImageNet: A Reality Check on Misclassification Classes

Reference 32

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Detecting Regional Spurious Correlations in Vision Transformers via Token Discarding ia − red2: Interpretability-aware redundancy reduction for vision transform- ers

Reference 33

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Detecting Regional Spurious Correlations in Vision Transformers via Token Discarding Automated classifi- cation of model errors on imagenet

Reference 34

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Detecting Regional Spurious Correlations in Vision Transformers via Token Discarding Finding and Fixing Spurious Patterns with Explanations

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Detecting Regional Spurious Correlations in Vision Transformers via Token Discarding Dynamicvit: Efficient vision transformers 19 with dynamic token sparsification

Reference 36

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Detecting Regional Spurious Correlations in Vision Transformers via Token Discarding Learning to Merge Tokens in Vision Transformers

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Detecting Regional Spurious Correlations in Vision Transformers via Token Discarding ” why should i trust you?” explaining the predictions of any classifier

Reference 38

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Detecting Regional Spurious Correlations in Vision Transformers via Token Discarding Berg, and Li Fei-Fei

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Detecting Regional Spurious Correlations in Vision Transformers via Token Discarding Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization

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Detecting Regional Spurious Correlations in Vision Transformers via Token Discarding Grad-cam: Visual explanations from deep networks via gradient-based localization

Reference 41

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Detecting Regional Spurious Correlations in Vision Transformers via Token Discarding Salient ImageNet: How to discover spurious features in Deep Learning?

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Detecting Regional Spurious Correlations in Vision Transformers via Token Discarding Axiomatic attribution for deep networks

Reference 43

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Detecting Regional Spurious Correlations in Vision Transformers via Token Discarding Rethinking the inception architec- ture for computer vision

Reference 44

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Detecting Regional Spurious Correlations in Vision Transformers via Token Discarding Over- coming simplicity bias in deep networks using a feature sieve

Reference 45

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Detecting Regional Spurious Correlations in Vision Transformers via Token Discarding Adversarial discrimina- tive domain adaptation

Reference 46

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This paper cites Attention is all you need.

Detecting Regional Spurious Correlations in Vision Transformers via Token Discarding Attention is all you need

Reference 47

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This paper cites Gen- eralizing to unseen domains: A survey on domain generalization.

Detecting Regional Spurious Correlations in Vision Transformers via Token Discarding Gen- eralizing to unseen domains: A survey on domain generalization

Reference 48

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Detecting Regional Spurious Correlations in Vision Transformers via Token Discarding Visu- alizing and understanding convolutional net- works

Reference 49

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