Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based Approach | Paper Citation Record

Observation b1d5fc6f-deb8-4d77-93ca-6b1d1d52374b · outbound

This paper cites Explaining explanations: An overview of interpretability of machine learning.

Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based Approach Explaining explanations: An overview of interpretability of machine learning

Reference 1

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This paper cites Overview of cnn research: 25 years history and the current trends.

Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based Approach Overview of cnn research: 25 years history and the current trends

Reference 2

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This paper cites EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks.

Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based Approach EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

Reference 3

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This paper cites Interpretable Explanations of Black Boxes by Meaningful Perturbation.

Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based Approach Interpretable Explanations of Black Boxes by Meaningful Perturbation

Reference 4

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This paper cites Guided Integrated Gradients: An Adaptive Path Method for Removing Noise.

Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based Approach Guided Integrated Gradients: An Adaptive Path Method for Removing Noise

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This paper cites Attribution in Scale and Space.

Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based Approach Attribution in Scale and Space

Reference 7

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This paper cites A survey on neural network interpretability.

Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based Approach A survey on neural network interpretability

Reference 8

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This paper cites Challenging the black box: A comprehensive evaluation of attribution maps of cnn applications in agriculture and forestry, 2024.

Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based Approach Challenging the black box: A comprehensive evaluation of attribution maps of cnn applications in agriculture and forestry, 2024

Reference 9

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This paper cites Convolutional networks for images, speech, and time series.

Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based Approach Convolutional networks for images, speech, and time series

Reference 10

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This paper cites Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps.

Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based Approach Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Reference 11

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Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based Approach Visualizing and Understanding Convolutional Networks

Reference 12

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Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based Approach Striving for Simplicity: The All Convolutional Net

Reference 13

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This paper cites SmoothGrad: removing noise by adding noise.

Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based Approach SmoothGrad: removing noise by adding noise

Reference 14

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This paper cites Axiomatic Attribution for Deep Networks.

Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based Approach Axiomatic Attribution for Deep Networks

Reference 15

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This paper cites Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra.

Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based Approach Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra

Reference 16

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This paper cites Grad- CAM ++: Generalized gradient-based visual explanations for deep convolutional networks.

Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based Approach Grad- CAM ++: Generalized gradient-based visual explanations for deep convolutional networks

Reference 17

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This paper cites Smooth Grad-CAM++: An Enhanced Inference Level Visualization Technique for Deep Convolutional Neural Network Models.

Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based Approach Smooth Grad-CAM++: An Enhanced Inference Level Visualization Technique for Deep Convolutional Neural Network Models

Reference 18

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This paper cites Layercam: Exploring hierarchical class activation maps for localization.

Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based Approach Layercam: Exploring hierarchical class activation maps for localization

Reference 19

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This paper cites Axiom-based Grad-CAM: Towards Accurate Visualization and Explanation of CNNs.

Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based Approach Axiom-based Grad-CAM: Towards Accurate Visualization and Explanation of CNNs

Reference 20

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This paper cites Score-cam: Score-weighted visual explanations for convolutional neural networks, 2020 a.

Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based Approach Score-cam: Score-weighted visual explanations for convolutional neural networks, 2020 a

Reference 21

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This paper cites Towards better understanding of gradient-based attribution methods for Deep Neural Networks.

Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based Approach Towards better understanding of gradient-based attribution methods for Deep Neural Networks

Reference 22

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Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based Approach SS-CAM: Smoothed Score-CAM for Sharper Visual Feature Localization

Reference 23

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Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based Approach IS-CAM: Integrated Score-CAM for axiomatic-based explanations

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Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based Approach Ramaswamy

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Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based Approach FD-CAM: Improving Faithfulness and Discriminability of Visual Explanation for CNNs

Reference 26

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Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based Approach Group-CAM: Group Score-Weighted Visual Explanations for Deep Convolutional Networks

Reference 27

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Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based Approach Poly-CAM: High resolution class activation map for convolutional neural networks

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Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based Approach Zoom-CAM: Generating Fine-grained Pixel Annotations from Image Labels

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Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based Approach Recipro-cam: Fast gradient-free visual explanations for convolutional neural networks, 2023

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Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based Approach Eigen-CAM: Class Activation Map using Principal Components

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Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based Approach RISE: Randomized Input Sampling for Explanation of Black-box Models

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Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based Approach Understanding Deep Networks via Extremal Perturbations and Smooth Masks

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Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based Approach "Why Should I Trust You?": Explaining the Predictions of Any Classifier

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Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based Approach Revisiting The Evaluation of Class Activation Mapping for Explainability: A Novel Metric and Experimental Analysis

Reference 35

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Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based Approach Metrics for saliency map evaluation of deep learning explanation methods

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Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based Approach Top-down Neural Attention by Excitation Backprop

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Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based Approach The Weighting Game: Evaluating Quality of Explainability Methods

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Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based Approach Unresolved cited work

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Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based Approach Microsoft COCO: Common Objects in Context

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Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based Approach Threat of adversarial attacks on deep learning in computer vision: A survey

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Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based Approach Foolbox: A Python toolbox to benchmark the robustness of machine learning models

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Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based Approach One pixel attack for fooling deep neural networks

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Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based Approach Towards Evaluating the Robustness of Neural Networks

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Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based Approach Detecting adversarial image examples in deep neural networks with adaptive noise reduction

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Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based Approach Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks

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Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based Approach A Review of Adversarial Attack and Defense for Classification Methods

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Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based Approach Explaining and Harnessing Adversarial Examples

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Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based Approach Towards Deep Learning Models Resistant to Adversarial Attacks

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Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based Approach A computational approach to edge detection

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Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based Approach Sanity checks for saliency maps

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Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based Approach Imagenet: A large-scale hierarchical image database

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Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based Approach Unresolved cited work

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Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based Approach Unresolved cited work

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Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based Approach Deep Residual Learning for Image Recognition

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Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based Approach Densely Connected Convolutional Networks

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Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based Approach A ConvNet for the 2020s

Reference 57

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This paper cites RepVGG: Making VGG-style ConvNets Great Again.

Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based Approach RepVGG: Making VGG-style ConvNets Great Again

Reference 58

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This paper cites Torchcam: class activation explorer.

Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based Approach Torchcam: class activation explorer

Reference 59

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This paper cites Saliency library.

Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based Approach Saliency library

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:46:13.004347Z

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Observation 846325ef-80f3-437d-ac40-24f047018e40 · outbound

This paper cites nonparametric.

Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based Approach nonparametric

Reference 61

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Observation d4e8f7fa-1d16-4464-a196-7ec540aef69a · outbound

This paper cites Towards deep learning models resistant to adversarial attacks, 2019.

Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based Approach Towards deep learning models resistant to adversarial attacks, 2019

Reference 62

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