Assessing the Noise Robustness of Class Activation Maps: A Framework for Reliable Model Interpretability | Paper Citation Record

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This paper cites Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead,.

Assessing the Noise Robustness of Class Activation Maps: A Framework for Reliable Model Interpretability Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead,

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This paper cites The role of explainability in creating trustworthy artificial intelligence for health care: A comprehen- sive survey of the terminology, design choices, and evaluation strategies,.

Assessing the Noise Robustness of Class Activation Maps: A Framework for Reliable Model Interpretability The role of explainability in creating trustworthy artificial intelligence for health care: A comprehen- sive survey of the terminology, design choices, and evaluation strategies,

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This paper cites Grad-cam++: Improved visual explanations for deep convolutional net- works,.

Assessing the Noise Robustness of Class Activation Maps: A Framework for Reliable Model Interpretability Grad-cam++: Improved visual explanations for deep convolutional net- works,

Reference 3

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This paper cites Axiom-based grad-cam: Towards accu- rate visual explanations for cnns,.

Assessing the Noise Robustness of Class Activation Maps: A Framework for Reliable Model Interpretability Axiom-based grad-cam: Towards accu- rate visual explanations for cnns,

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This paper cites Ablation-cam: Visual expla- nations for deep convolutional networks via gradient-free localization,.

Assessing the Noise Robustness of Class Activation Maps: A Framework for Reliable Model Interpretability Ablation-cam: Visual expla- nations for deep convolutional networks via gradient-free localization,

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This paper cites Hirescam: High-resolution class activa- tion mapping,.

Assessing the Noise Robustness of Class Activation Maps: A Framework for Reliable Model Interpretability Hirescam: High-resolution class activa- tion mapping,

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This paper cites Eigen-cam: Class activation mapping using prin- cipal components,.

Assessing the Noise Robustness of Class Activation Maps: A Framework for Reliable Model Interpretability Eigen-cam: Class activation mapping using prin- cipal components,

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This paper cites Revisiting the evaluation of class activation mapping for explainability: A novel metric and experi- mental analysis,.

Assessing the Noise Robustness of Class Activation Maps: A Framework for Reliable Model Interpretability Revisiting the evaluation of class activation mapping for explainability: A novel metric and experi- mental analysis,

Reference 8

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This paper cites Towards trustable explainable ai,.

Assessing the Noise Robustness of Class Activation Maps: A Framework for Reliable Model Interpretability Towards trustable explainable ai,

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This paper cites Rethinking Positive Aggregation and Propagation of Gradients in Gradient-based Saliency Methods.

Assessing the Noise Robustness of Class Activation Maps: A Framework for Reliable Model Interpretability Rethinking Positive Aggregation and Propagation of Gradients in Gradient-based Saliency Methods

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This paper cites Procrustes-based dis- tances for exploring between-matrices similarity,.

Assessing the Noise Robustness of Class Activation Maps: A Framework for Reliable Model Interpretability Procrustes-based dis- tances for exploring between-matrices similarity,

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This paper cites Framework for evaluating faithfulness of local explanations,.

Assessing the Noise Robustness of Class Activation Maps: A Framework for Reliable Model Interpretability Framework for evaluating faithfulness of local explanations,

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This paper cites On the robustness of interpretability methods,.

Assessing the Noise Robustness of Class Activation Maps: A Framework for Reliable Model Interpretability On the robustness of interpretability methods,

Reference 13

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This paper cites Rethinking Stability for Attribution-based Explanations.

Assessing the Noise Robustness of Class Activation Maps: A Framework for Reliable Model Interpretability Rethinking Stability for Attribution-based Explanations

Reference 14

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This paper cites A similarity measure for indefinite rankings,.

Assessing the Noise Robustness of Class Activation Maps: A Framework for Reliable Model Interpretability A similarity measure for indefinite rankings,

Reference 15

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

Assessing the Noise Robustness of Class Activation Maps: A Framework for Reliable Model Interpretability Grad-cam: Visual explanations from deep networks via gradient-based localization,

Reference 16

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This paper cites Ex- plaining nonlinear classification decisions with deep taylor decomposition,.

Assessing the Noise Robustness of Class Activation Maps: A Framework for Reliable Model Interpretability Ex- plaining nonlinear classification decisions with deep taylor decomposition,

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This paper cites Pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation,.

Assessing the Noise Robustness of Class Activation Maps: A Framework for Reliable Model Interpretability Pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation,

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This paper cites Evaluating the visualization of what a deep neural network has learned,.

Assessing the Noise Robustness of Class Activation Maps: A Framework for Reliable Model Interpretability Evaluating the visualization of what a deep neural network has learned,

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This paper cites What is relevant in a text document?: Learning relevance in document representa- tions,.

Assessing the Noise Robustness of Class Activation Maps: A Framework for Reliable Model Interpretability What is relevant in a text document?: Learning relevance in document representa- tions,

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This paper cites On the (in)fidelity and sensitivity of explanations,.

Assessing the Noise Robustness of Class Activation Maps: A Framework for Reliable Model Interpretability On the (in)fidelity and sensitivity of explanations,

Reference 21

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This paper cites Sanity checks for saliency maps,.

Assessing the Noise Robustness of Class Activation Maps: A Framework for Reliable Model Interpretability Sanity checks for saliency maps,

Reference 22

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This paper cites How good is your expla- nation? algorithmic stability measures to assess the quality of explanations for deep neural networks,.

Assessing the Noise Robustness of Class Activation Maps: A Framework for Reliable Model Interpretability How good is your expla- nation? algorithmic stability measures to assess the quality of explanations for deep neural networks,

Reference 23

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This paper cites A benchmark for in- terpretability methods in deep neural networks,.

Assessing the Noise Robustness of Class Activation Maps: A Framework for Reliable Model Interpretability A benchmark for in- terpretability methods in deep neural networks,

Reference 24

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This paper cites Ex- plaining deep neural networks and beyond: A review of methods and appli- cations,.

Assessing the Noise Robustness of Class Activation Maps: A Framework for Reliable Model Interpretability Ex- plaining deep neural networks and beyond: A review of methods and appli- cations,

Reference 25

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This paper cites Sam: the sensitivity of attribution methods to hyperparameters. in 2020 ieee,.

Assessing the Noise Robustness of Class Activation Maps: A Framework for Reliable Model Interpretability Sam: the sensitivity of attribution methods to hyperparameters. in 2020 ieee,

Reference 26

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Assessing the Noise Robustness of Class Activation Maps: A Framework for Reliable Model Interpretability How explainable are adversarially-robust CNNs?

Reference 27

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This paper cites Sanity checks for saliency maps,.

Assessing the Noise Robustness of Class Activation Maps: A Framework for Reliable Model Interpretability Sanity checks for saliency maps,

Reference 28

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This paper cites On the (in)fidelity and sensitivity of explanations,.

Assessing the Noise Robustness of Class Activation Maps: A Framework for Reliable Model Interpretability On the (in)fidelity and sensitivity of explanations,

Reference 29

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Assessing the Noise Robustness of Class Activation Maps: A Framework for Reliable Model Interpretability Deep residual learning for image recognition,

Reference 30

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Assessing the Noise Robustness of Class Activation Maps: A Framework for Reliable Model Interpretability Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 31

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Assessing the Noise Robustness of Class Activation Maps: A Framework for Reliable Model Interpretability Rethinking the inception architecture for computer vision,

Reference 32

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

Assessing the Noise Robustness of Class Activation Maps: A Framework for Reliable Model Interpretability An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 33

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This paper cites Asirra: A captcha that ex- ploits interest-aligned manual image categorization,.

Assessing the Noise Robustness of Class Activation Maps: A Framework for Reliable Model Interpretability Asirra: A captcha that ex- ploits interest-aligned manual image categorization,

Reference 34

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Assessing the Noise Robustness of Class Activation Maps: A Framework for Reliable Model Interpretability Ima- geNet Large Scale Visual Recognition Challenge,

Reference 35

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Assessing the Noise Robustness of Class Activation Maps: A Framework for Reliable Model Interpretability Cats and dogs,

Reference 36

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This paper cites The ham10000 dataset: A large collection of multi-source dermatoscopic images of common pigmented skin lesions,.

Assessing the Noise Robustness of Class Activation Maps: A Framework for Reliable Model Interpretability The ham10000 dataset: A large collection of multi-source dermatoscopic images of common pigmented skin lesions,

Reference 37

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This paper cites Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories,.

Assessing the Noise Robustness of Class Activation Maps: A Framework for Reliable Model Interpretability Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories,

Reference 38

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Assessing the Noise Robustness of Class Activation Maps: A Framework for Reliable Model Interpretability Quick shift and kernel methods for mode seek- ing,

Reference 39

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This paper cites A similarity measure for indefinite rankings,.

Assessing the Noise Robustness of Class Activation Maps: A Framework for Reliable Model Interpretability A similarity measure for indefinite rankings,

Reference 40

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This paper cites An image is worth 16x16 words: Transformers for image recognition at scale,.

Assessing the Noise Robustness of Class Activation Maps: A Framework for Reliable Model Interpretability An image is worth 16x16 words: Transformers for image recognition at scale,

Reference 41

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Assessing the Noise Robustness of Class Activation Maps: A Framework for Reliable Model Interpretability Icev2: Interpretability, comprehensiveness, and explainability in vision transformer,

Reference 42

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This paper cites JPEG Compression Standard,.

Assessing the Noise Robustness of Class Activation Maps: A Framework for Reliable Model Interpretability JPEG Compression Standard,

Reference 43

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This paper cites Comparison of direct blind deconvolu- tion methods for motion-blurred images,.

Assessing the Noise Robustness of Class Activation Maps: A Framework for Reliable Model Interpretability Comparison of direct blind deconvolu- tion methods for motion-blurred images,

Reference 44

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This paper cites Explaining and Harnessing Adversarial Examples.

Assessing the Noise Robustness of Class Activation Maps: A Framework for Reliable Model Interpretability Explaining and Harnessing Adversarial Examples

Reference 45

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This paper cites Towards Deep Learning Models Resistant to Adversarial Attacks.

Assessing the Noise Robustness of Class Activation Maps: A Framework for Reliable Model Interpretability Towards Deep Learning Models Resistant to Adversarial Attacks

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This paper cites Towards evaluating the robustness of neural net- works,.

Assessing the Noise Robustness of Class Activation Maps: A Framework for Reliable Model Interpretability Towards evaluating the robustness of neural net- works,

Reference 47

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This paper cites Slic superpixels compared to state-of-the-art superpixel methods,.

Assessing the Noise Robustness of Class Activation Maps: A Framework for Reliable Model Interpretability Slic superpixels compared to state-of-the-art superpixel methods,

Reference 48

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This paper cites Efficient graph-based image 42 segmentation,.

Assessing the Noise Robustness of Class Activation Maps: A Framework for Reliable Model Interpretability Efficient graph-based image 42 segmentation,

Reference 49

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This paper cites A new measure of rank correlation,.

Assessing the Noise Robustness of Class Activation Maps: A Framework for Reliable Model Interpretability A new measure of rank correlation,

Reference 50

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This paper cites The proof and measurement of association between two things,.

Assessing the Noise Robustness of Class Activation Maps: A Framework for Reliable Model Interpretability The proof and measurement of association between two things,

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This paper cites The problem of m rankings,.

Assessing the Noise Robustness of Class Activation Maps: A Framework for Reliable Model Interpretability The problem of m rankings,

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This paper cites Smoothgrad: removing noise by adding noise,.

Assessing the Noise Robustness of Class Activation Maps: A Framework for Reliable Model Interpretability Smoothgrad: removing noise by adding noise,

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This paper cites Probabilistic pixel attribution: Interpreting deep models with statistical inference,.

Assessing the Noise Robustness of Class Activation Maps: A Framework for Reliable Model Interpretability Probabilistic pixel attribution: Interpreting deep models with statistical inference,

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This paper cites Towards deep learning models resistant to adversarial attacks,.

Assessing the Noise Robustness of Class Activation Maps: A Framework for Reliable Model Interpretability Towards deep learning models resistant to adversarial attacks,

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