Advancing Attribution-Based Neural Network Explainability through Relative Absolute Magnitude Layer-Wise Relevance Propagation and Multi-Component Evaluation | Paper Citation Record

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Advancing Attribution-Based Neural Network Explainability through Relative Absolute Magnitude Layer-Wise Relevance Propagation and Multi-Component Evaluation On the Robustness of Interpretability Methods

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Advancing Attribution-Based Neural Network Explainability through Relative Absolute Magnitude Layer-Wise Relevance Propagation and Multi-Component Evaluation Ground Truth Evaluation of Neural Network Explanations with CLEVR-XAI

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This paper cites One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques.

Advancing Attribution-Based Neural Network Explainability through Relative Absolute Magnitude Layer-Wise Relevance Propagation and Multi-Component Evaluation One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques

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Advancing Attribution-Based Neural Network Explainability through Relative Absolute Magnitude Layer-Wise Relevance Propagation and Multi-Component Evaluation Evaluating and Aggregating Feature-based Model Explanations

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Advancing Attribution-Based Neural Network Explainability through Relative Absolute Magnitude Layer-Wise Relevance Propagation and Multi-Component Evaluation Framework for Evaluating Faithfulness of Local Explanations

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Advancing Attribution-Based Neural Network Explainability through Relative Absolute Magnitude Layer-Wise Relevance Propagation and Multi-Component Evaluation Use HiResCAM instead of Grad-CAM for faithful explanations of convolutional neural networks

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Advancing Attribution-Based Neural Network Explainability through Relative Absolute Magnitude Layer-Wise Relevance Propagation and Multi-Component Evaluation Everingham, L

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Advancing Attribution-Based Neural Network Explainability through Relative Absolute Magnitude Layer-Wise Relevance Propagation and Multi-Component Evaluation Relative Attributing Propagation: Interpreting the Comparative Contributions of Individual Units in Deep Neural Networks

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Advancing Attribution-Based Neural Network Explainability through Relative Absolute Magnitude Layer-Wise Relevance Propagation and Multi-Component Evaluation On quantitative aspects of model interpretability

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Advancing Attribution-Based Neural Network Explainability through Relative Absolute Magnitude Layer-Wise Relevance Propagation and Multi-Component Evaluation Why Should I Trust You?

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Advancing Attribution-Based Neural Network Explainability through Relative Absolute Magnitude Layer-Wise Relevance Propagation and Multi-Component Evaluation IROF: a low resource evaluation metric for explanation methods

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Advancing Attribution-Based Neural Network Explainability through Relative Absolute Magnitude Layer-Wise Relevance Propagation and Multi-Component Evaluation In Proceedings of the 38th International Conference on Machine Learning, ICML 2021 (Proceedings of Machine Learning Research, Vol

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Advancing Attribution-Based Neural Network Explainability through Relative Absolute Magnitude Layer-Wise Relevance Propagation and Multi-Component Evaluation Berg, and Li Fei-Fei

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Advancing Attribution-Based Neural Network Explainability through Relative Absolute Magnitude Layer-Wise Relevance Propagation and Multi-Component Evaluation A Consistent and Efficient Evaluation Strategy for Attribution Methods

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Advancing Attribution-Based Neural Network Explainability through Relative Absolute Magnitude Layer-Wise Relevance Propagation and Multi-Component Evaluation Not Just a Black Box: Learning Important Features Through Propagating Activation Differences

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Advancing Attribution-Based Neural Network Explainability through Relative Absolute Magnitude Layer-Wise Relevance Propagation and Multi-Component Evaluation Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra

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Advancing Attribution-Based Neural Network Explainability through Relative Absolute Magnitude Layer-Wise Relevance Propagation and Multi-Component Evaluation SmoothGrad: removing noise by adding noise

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Advancing Attribution-Based Neural Network Explainability through Relative Absolute Magnitude Layer-Wise Relevance Propagation and Multi-Component Evaluation Gomez, Lukasz Kaiser, and Illia Polosukhin

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Advancing Attribution-Based Neural Network Explainability through Relative Absolute Magnitude Layer-Wise Relevance Propagation and Multi-Component Evaluation Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

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Advancing Attribution-Based Neural Network Explainability through Relative Absolute Magnitude Layer-Wise Relevance Propagation and Multi-Component Evaluation Courville, Ruslan Salakhutdinov, Richard S

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Advancing Attribution-Based Neural Network Explainability through Relative Absolute Magnitude Layer-Wise Relevance Propagation and Multi-Component Evaluation Zeiler and Rob Fergus

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Advancing Attribution-Based Neural Network Explainability through Relative Absolute Magnitude Layer-Wise Relevance Propagation and Multi-Component Evaluation Unresolved cited work

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This paper cites PLOS ONE 10, 7 (jul 2015), e0130140.

Advancing Attribution-Based Neural Network Explainability through Relative Absolute Magnitude Layer-Wise Relevance Propagation and Multi-Component Evaluation PLOS ONE 10, 7 (jul 2015), e0130140

Reference 2015

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This paper cites Nature 529, 7587 (Jan.

Advancing Attribution-Based Neural Network Explainability through Relative Absolute Magnitude Layer-Wise Relevance Propagation and Multi-Component Evaluation Nature 529, 7587 (Jan

Reference 2016

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This paper cites International Journal of Computer Vision 128, 2 (oct 2019), 336–359.

Advancing Attribution-Based Neural Network Explainability through Relative Absolute Magnitude Layer-Wise Relevance Propagation and Multi-Component Evaluation International Journal of Computer Vision 128, 2 (oct 2019), 336–359

Reference 2019

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This paper cites https://openreview.net/forum?id=XPZIaotutsD.

Advancing Attribution-Based Neural Network Explainability through Relative Absolute Magnitude Layer-Wise Relevance Propagation and Multi-Component Evaluation https://openreview.net/forum?id=XPZIaotutsD

Reference 2021

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