Consistent Evidence, Robust Recognition: Faithful Attribution Regularization under Geometric Transformations | Paper Citation Record

Observation 66242aa2-9dab-4f25-bcdd-6e090d36bfbd · outbound

This paper cites Where mllms attend and what they rely on: Explaining autoregressive token generation,.

Consistent Evidence, Robust Recognition: Faithful Attribution Regularization under Geometric Transformations Where mllms attend and what they rely on: Explaining autoregressive token generation,

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This paper cites Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection.

Consistent Evidence, Robust Recognition: Faithful Attribution Regularization under Geometric Transformations Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection

Reference 2

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Consistent Evidence, Robust Recognition: Faithful Attribution Regularization under Geometric Transformations Axiomatic attribution for deep networks,

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This paper cites Ig 2: Integrated gradient on iterative gradient path for feature attribution,.

Consistent Evidence, Robust Recognition: Faithful Attribution Regularization under Geometric Transformations Ig 2: Integrated gradient on iterative gradient path for feature attribution,

Reference 4

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Consistent Evidence, Robust Recognition: Faithful Attribution Regularization under Geometric Transformations Path choice matters for clear attributions in path methods,

Reference 5

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This paper cites Gradient-based visual explanation for transformer-based CLIP,.

Consistent Evidence, Robust Recognition: Faithful Attribution Regularization under Geometric Transformations Gradient-based visual explanation for transformer-based CLIP,

Reference 6

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This paper cites A unified approach to interpreting model predictions,.

Consistent Evidence, Robust Recognition: Faithful Attribution Regularization under Geometric Transformations A unified approach to interpreting model predictions,

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This paper cites Going beyond XAI: A systematic survey for explanation-guided learning,.

Consistent Evidence, Robust Recognition: Faithful Attribution Regularization under Geometric Transformations Going beyond XAI: A systematic survey for explanation-guided learning,

Reference 8

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This paper cites Generalized semantic contrastive learning via embedding side information for few- shot object detection,.

Consistent Evidence, Robust Recognition: Faithful Attribution Regularization under Geometric Transformations Generalized semantic contrastive learning via embedding side information for few- shot object detection,

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Observation 1cd6afef-5da0-43cc-93ba-654b1e848313 · outbound

This paper cites Where Not to Learn: Prior-Aligned Training with Subset-based Attribution Constraints for Reliable Decision-Making.

Consistent Evidence, Robust Recognition: Faithful Attribution Regularization under Geometric Transformations Where Not to Learn: Prior-Aligned Training with Subset-based Attribution Constraints for Reliable Decision-Making

Reference 10

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This paper cites Underspec- ification presents challenges for credibility in modern machine learning,.

Consistent Evidence, Robust Recognition: Faithful Attribution Regularization under Geometric Transformations Underspec- ification presents challenges for credibility in modern machine learning,

Reference 11

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This paper cites Imagenet-trained cnns are biased towards texture; in- creasing shape bias improves accuracy and robustness,.

Consistent Evidence, Robust Recognition: Faithful Attribution Regularization under Geometric Transformations Imagenet-trained cnns are biased towards texture; in- creasing shape bias improves accuracy and robustness,

Reference 12

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Observation c143e242-57d3-4216-a1b1-4d78e389c4fd · outbound

This paper cites Evaluating the robustness of inter- pretability methods through explanation invariance and equivariance,.

Consistent Evidence, Robust Recognition: Faithful Attribution Regularization under Geometric Transformations Evaluating the robustness of inter- pretability methods through explanation invariance and equivariance,

Reference 13

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This paper cites Hive: Evaluating the human interpretability of visual explanations,.

Consistent Evidence, Robust Recognition: Faithful Attribution Regularization under Geometric Transformations Hive: Evaluating the human interpretability of visual explanations,

Reference 15

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This paper cites Making deep neural networks right for the right scientific reasons by interacting with their explanations,.

Consistent Evidence, Robust Recognition: Faithful Attribution Regularization under Geometric Transformations Making deep neural networks right for the right scientific reasons by interacting with their explanations,

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This paper cites Right for the right rea- sons: Training differentiable models by constraining their explanations,.

Consistent Evidence, Robust Recognition: Faithful Attribution Regularization under Geometric Transformations Right for the right rea- sons: Training differentiable models by constraining their explanations,

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This paper cites Taking a HINT: leveraging explanations to make vision and language models more grounded,.

Consistent Evidence, Robust Recognition: Faithful Attribution Regularization under Geometric Transformations Taking a HINT: leveraging explanations to make vision and language models more grounded,

Reference 18

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This paper cites Improving visual grounding by encouraging consistent gradient-based explanations,.

Consistent Evidence, Robust Recognition: Faithful Attribution Regularization under Geometric Transformations Improving visual grounding by encouraging consistent gradient-based explanations,

Reference 19

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This paper cites Explainable models with consistent inter- pretations,.

Consistent Evidence, Robust Recognition: Faithful Attribution Regularization under Geometric Transformations Explainable models with consistent inter- pretations,

Reference 20

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This paper cites Consistent explanations by contrastive learning,.

Consistent Evidence, Robust Recognition: Faithful Attribution Regularization under Geometric Transformations Consistent explanations by contrastive learning,

Reference 21

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Observation 62aa879c-0c3a-4c02-8d13-80bdb8e61c1a · outbound

This paper cites Are data-driven explanations robust against out-of-distribution data?.

Consistent Evidence, Robust Recognition: Faithful Attribution Regularization under Geometric Transformations Are data-driven explanations robust against out-of-distribution data?

Reference 22

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Consistent Evidence, Robust Recognition: Faithful Attribution Regularization under Geometric Transformations ICEL: learning with inconsistent expla- nations,

Reference 23

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This paper cites Grad-cam: Visual explanations from deep networks via gradient-based localization,.

Consistent Evidence, Robust Recognition: Faithful Attribution Regularization under Geometric Transformations Grad-cam: Visual explanations from deep networks via gradient-based localization,

Reference 24

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This paper cites Sanity checks for saliency maps,.

Consistent Evidence, Robust Recognition: Faithful Attribution Regularization under Geometric Transformations Sanity checks for saliency maps,

Reference 25

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Observation ee42f2a0-f08b-42a9-91ea-d3ca37886bea · outbound

This paper cites On the relationship between explanation and prediction: A causal view,.

Consistent Evidence, Robust Recognition: Faithful Attribution Regularization under Geometric Transformations On the relationship between explanation and prediction: A causal view,

Reference 26

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This paper cites Less is more: Fewer interpretable region via submodular subset selection,.

Consistent Evidence, Robust Recognition: Faithful Attribution Regularization under Geometric Transformations Less is more: Fewer interpretable region via submodular subset selection,

Reference 27

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This paper cites One explanation is not enough: structured attention graphs for image classification,.

Consistent Evidence, Robust Recognition: Faithful Attribution Regularization under Geometric Transformations One explanation is not enough: structured attention graphs for image classification,

Reference 28

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This paper cites Identifying important group of pixels using interactions,.

Consistent Evidence, Robust Recognition: Faithful Attribution Regularization under Geometric Transformations Identifying important group of pixels using interactions,

Reference 29

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This paper cites Interpreting object-level foundation models via visual precision search,.

Consistent Evidence, Robust Recognition: Faithful Attribution Regularization under Geometric Transformations Interpreting object-level foundation models via visual precision search,

Reference 30

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This paper cites Grounding DINO: marrying DINO with grounded pre-training for open-set object detection,.

Consistent Evidence, Robust Recognition: Faithful Attribution Regularization under Geometric Transformations Grounding DINO: marrying DINO with grounded pre-training for open-set object detection,

Reference 31

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Consistent Evidence, Robust Recognition: Faithful Attribution Regularization under Geometric Transformations Florence-2: Advancing a unified representation for a variety of vision tasks,

Reference 32

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Consistent Evidence, Robust Recognition: Faithful Attribution Regularization under Geometric Transformations On the Robustness of Interpretability Methods

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Consistent Evidence, Robust Recognition: Faithful Attribution Regularization under Geometric Transformations Interpretation of neural networks is fragile,

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This paper cites Evaluating the robustness of inter- pretability methods through explanation invariance and equivariance,.

Consistent Evidence, Robust Recognition: Faithful Attribution Regularization under Geometric Transformations Evaluating the robustness of inter- pretability methods through explanation invariance and equivariance,

Reference 35

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Consistent Evidence, Robust Recognition: Faithful Attribution Regularization under Geometric Transformations An analysis of approximations for maximizing submodular set functions - I,

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Consistent Evidence, Robust Recognition: Faithful Attribution Regularization under Geometric Transformations Boosting the visual interpretability of CLIP via adversarial fine-tuning,

Reference 37

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Consistent Evidence, Robust Recognition: Faithful Attribution Regularization under Geometric Transformations Imagenet: A large-scale hierarchical image database,

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Consistent Evidence, Robust Recognition: Faithful Attribution Regularization under Geometric Transformations Are we done with ImageNet?

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Consistent Evidence, Robust Recognition: Faithful Attribution Regularization under Geometric Transformations Do imagenet classifiers generalize to imagenet?

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Consistent Evidence, Robust Recognition: Faithful Attribution Regularization under Geometric Transformations Benchmarking neural network robustness to common corrup- tions and perturbations,

Reference 41

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Consistent Evidence, Robust Recognition: Faithful Attribution Regularization under Geometric Transformations Objectnet: A large-scale bias-controlled dataset for pushing the limits of object recognition models,

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Consistent Evidence, Robust Recognition: Faithful Attribution Regularization under Geometric Transformations Learning robust global representations by penalizing local predictive power,

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Consistent Evidence, Robust Recognition: Faithful Attribution Regularization under Geometric Transformations The many faces of robustness: A critical analysis of out-of-distribution generalization,

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Consistent Evidence, Robust Recognition: Faithful Attribution Regularization under Geometric Transformations Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness,

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Consistent Evidence, Robust Recognition: Faithful Attribution Regularization under Geometric Transformations Natural adversarial examples,

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Consistent Evidence, Robust Recognition: Faithful Attribution Regularization under Geometric Transformations Leaving reality to imagination: Robust classification via generated datasets,

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This paper cites Are data-driven explanations robust against out-of-distribution data?.

Consistent Evidence, Robust Recognition: Faithful Attribution Regularization under Geometric Transformations Are data-driven explanations robust against out-of-distribution data?

Reference 48

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Consistent Evidence, Robust Recognition: Faithful Attribution Regularization under Geometric Transformations Training for stable explanation for free,

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Consistent Evidence, Robust Recognition: Faithful Attribution Regularization under Geometric Transformations Explanation-guided adversarial training for robust and interpretable models,

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Consistent Evidence, Robust Recognition: Faithful Attribution Regularization under Geometric Transformations Rise: Randomized input sampling for explanation of black-box models,

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This paper cites Benchmarking deletion metrics with the principled explanations,.

Consistent Evidence, Robust Recognition: Faithful Attribution Regularization under Geometric Transformations Benchmarking deletion metrics with the principled explanations,

Reference 52

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This paper cites G-CAME: Gaussian-Class Activation Mapping Explainer for Object Detectors.

Consistent Evidence, Robust Recognition: Faithful Attribution Regularization under Geometric Transformations G-CAME: Gaussian-Class Activation Mapping Explainer for Object Detectors

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This paper cites How to probe: Simple yet effective techniques for improving post-hoc explanations,.

Consistent Evidence, Robust Recognition: Faithful Attribution Regularization under Geometric Transformations How to probe: Simple yet effective techniques for improving post-hoc explanations,

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This paper cites Deep residual learning for image recognition,.

Consistent Evidence, Robust Recognition: Faithful Attribution Regularization under Geometric Transformations Deep residual learning for image recognition,

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Consistent Evidence, Robust Recognition: Faithful Attribution Regularization under Geometric Transformations A convnet for the 2020s,

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