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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:46:13.097171Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 6f8d4322-6f92-4ce4-9b42-a4f83a924b8a · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:46:13.090077Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 0c1f5520-a654-4375-9d47-b024cf46ed91 · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-12T14:46:12.191414Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 048f5286-e2f5-4aba-a2ea-f30c3be46e3b · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-12T14:46:12.194568Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ce16f47c-0a17-4cbf-b6be-138921155aa9 · outbound

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

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-12T14:46:12.201030Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5355345b-2204-4c42-9ed6-7436c6fbb08f · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-12T14:46:12.204104Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 77810fa8-6b66-49a0-aca4-ff8b12fad4c7 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:46:13.082426Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a019035a-6207-478a-a9cb-65af1b503f31 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:46:13.075273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ac9b9fec-2107-4215-9227-9fe2c52c1a63 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:46:13.068307Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation df942a52-2a0e-4117-bd21-a321de2f0e51 · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-12T14:46:12.215784Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2ed1f3a0-f567-4820-85cd-306e0e0c5c61 · outbound

This paper cites Visualizing and Understanding Convolutional Networks.

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

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-12T14:46:12.219069Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c0f8a2f0-b2d9-4850-b857-458207452dc6 · outbound

This paper cites Striving for Simplicity: The All Convolutional Net.

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

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-12T14:46:12.222173Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b3b74c5f-3272-4dfd-8227-7723a2424708 · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-12T14:46:12.225419Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2eb80369-5397-44ce-9afd-f5c31d56a78d · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-12T14:46:12.228309Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fb02ec87-d6d3-4f39-aae3-e3b9ea6b824a · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-12T14:46:12.231267Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation adb517d0-8856-4f9e-b822-bbcfad411b2b · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-12T14:46:12.234580Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7fb42600-e942-463f-8f47-dfa2a9960f41 · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-12T14:46:12.237819Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bda1f54c-f433-44a0-975c-c3845df04adb · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-12T14:46:12.240848Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c4e3d39d-20c9-4967-987c-b4a547ee614c · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-12T14:46:12.243374Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7d9e37d4-5137-44c2-b4ef-8e70c99f8d9a · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:46:13.061328Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 7ce18e93-d7d2-4d9c-ba79-8cbe85c9074f · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-12T14:46:12.248901Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 27cb2151-2d95-4a9e-a262-cb49ac54ede2 · outbound

This paper cites SS-CAM: Smoothed Score-CAM for Sharper Visual Feature Localization.

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

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-12T14:46:12.251664Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fa08391e-a5a6-4bd1-a690-9ed33300975a · outbound

This paper cites IS-CAM: Integrated Score-CAM for axiomatic-based explanations.

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

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-12T14:46:12.254110Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8b3347f9-a7ce-48a8-9738-82c3e2dd366a · outbound

This paper cites Ramaswamy.

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

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-12T14:46:12.256785Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9eb78607-4ab4-49d7-a38f-fce49af502fe · outbound

This paper cites FD-CAM: Improving Faithfulness and Discriminability of Visual Explanation for CNNs.

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

Reference 26

Resolution: verified exact; local_arxiv, observed 2026-08-12T14:46:12.696616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 11836526-7bba-49c6-b17b-f78fd96957b3 · outbound

This paper cites Group-CAM: Group Score-Weighted Visual Explanations for Deep Convolutional Networks.

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

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-12T14:46:12.261907Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 83b77de3-dc28-417a-8b0a-2c3a730730b9 · outbound

This paper cites Poly-CAM: High resolution class activation map for convolutional neural networks.

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

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-08-12T14:46:12.680743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 0806dba8-f3c3-40f9-8ec7-9f33c56643dd · outbound

This paper cites Zoom-CAM: Generating Fine-grained Pixel Annotations from Image Labels.

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

Reference 29

Resolution: verified exact; local_arxiv, observed 2026-08-12T14:46:12.671189Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c1ef4996-d49b-4c07-90c2-fda28751e9aa · outbound

This paper cites Recipro-cam: Fast gradient-free visual explanations for convolutional neural networks, 2023.

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

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:46:13.054193Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4cf71907-6636-4b02-958a-29dd79be85da · outbound

This paper cites Eigen-CAM: Class Activation Map using Principal Components.

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

Reference 31

Resolution: verified exact; local_arxiv, observed 2026-08-12T14:46:12.660978Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5802df91-78b3-4073-9485-1a3ad70ca27d · outbound

This paper cites RISE: Randomized Input Sampling for Explanation of Black-box Models.

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

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-12T14:46:12.276412Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2d22d087-5d87-4be6-a8a2-c0d61e990f60 · outbound

This paper cites Understanding Deep Networks via Extremal Perturbations and Smooth Masks.

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

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-12T14:46:12.279082Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b9f7af9b-50cc-4e8a-8451-c224293fabd8 · outbound

This paper cites "Why Should I Trust You?": Explaining the Predictions of Any Classifier.

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

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-12T14:46:12.282205Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3eb075c0-fbca-4c06-b320-7199d37c9699 · outbound

This paper cites Revisiting The Evaluation of Class Activation Mapping for Explainability: A Novel Metric and Experimental Analysis.

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

Resolution: verified exact; local_arxiv, observed 2026-08-12T14:46:12.638389Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 7840f774-354e-4f55-bf33-88a894229ce0 · outbound

This paper cites Metrics for saliency map evaluation of deep learning explanation methods.

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

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-12T14:46:12.287423Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1ae36e9f-c1d4-47c3-afeb-84984630064a · outbound

This paper cites Top-down Neural Attention by Excitation Backprop.

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

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-12T14:46:12.290231Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cfa7a902-7923-43d1-9a91-a8a08d8ac781 · outbound

This paper cites The Weighting Game: Evaluating Quality of Explainability Methods.

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

Reference 38

Resolution: verified exact; local_arxiv, observed 2026-08-12T14:46:12.616788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 9c5f0cd7-6bff-4992-8267-516fe761c419 · outbound

This paper cites an unresolved cited work.

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

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-12T14:46:12.295808Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6950ec2b-bd6f-44bd-b2de-1f3013970ba9 · outbound

This paper cites Microsoft COCO: Common Objects in Context.

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

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-12T14:46:12.298342Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6547326c-6a97-45d6-8c9f-aee21bcf5f2c · outbound

This paper cites Threat of adversarial attacks on deep learning in computer vision: A survey.

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

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:46:13.046522Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d66aab4f-8c12-4e0d-90db-33a8965f7bc1 · outbound

This paper cites Foolbox: A Python toolbox to benchmark the robustness of machine learning models.

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

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-12T14:46:12.303939Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cc1eec0f-74bc-490c-9374-eb1dff1a259a · outbound

This paper cites One pixel attack for fooling deep neural networks.

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

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-12T14:46:12.306609Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5ffc6531-e2bf-4b11-819c-a7128df421d9 · outbound

This paper cites Towards Evaluating the Robustness of Neural Networks.

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

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-12T14:46:12.309521Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fcdebc18-d9f9-460d-9ecb-50ba7738fbcb · outbound

This paper cites Detecting adversarial image examples in deep neural networks with adaptive noise reduction.

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

Reference 45

Resolution: metadata mismatch; raw_fallback, observed 2026-08-12T14:46:12.582071Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2827293e-eabd-419d-9825-000cf6cfaf9b · outbound

This paper cites Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks.

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

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-12T14:46:12.314674Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 56e3047c-a0d0-481a-8c79-85fdaa4c5bf5 · outbound

This paper cites A Review of Adversarial Attack and Defense for Classification Methods.

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

Reference 47

Resolution: verified exact; local_arxiv, observed 2026-08-12T14:46:12.523120Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4bd8baa0-c58a-41b9-a5a4-10678570b2ae · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

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

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-12T14:46:12.320089Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1163b655-7ca3-40e1-b81b-fe201d04cb8c · outbound

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

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

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-12T14:46:12.322436Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e08961cd-622a-4bc0-832a-2fe8d0ed0cee · outbound

This paper cites A computational approach to edge detection.

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

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:46:13.038983Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 7af3cafe-4e94-4c27-81b0-b657baed84ca · outbound

This paper cites Sanity checks for saliency maps.

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

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:46:13.031370Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4398dce1-8cdf-4717-a339-59f96161676a · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

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

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-12T14:46:12.330026Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9eb6b619-f7c8-411f-b990-904d16a931b9 · outbound

This paper cites an unresolved cited work.

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

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-12T14:46:12.332881Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c7241323-0eaa-4247-a6a8-e9db72631248 · outbound

This paper cites an unresolved cited work.

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

Reference 54

Resolution: unresolved; raw_fallback, observed 2026-08-12T14:46:13.019824Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4fea1195-2792-434a-a9be-90396d6cebd0 · outbound

This paper cites Deep Residual Learning for Image Recognition.

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

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-12T14:46:12.338184Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dbcd47a8-ccba-4301-858a-050b2a2e271c · outbound

This paper cites Densely Connected Convolutional Networks.

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

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-12T14:46:12.341287Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9ca60266-d840-44bb-8678-27ade01131d0 · outbound

This paper cites A ConvNet for the 2020s.

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

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-12T14:46:12.344565Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f69a5874-5e6b-4a73-9351-217b7cf8f562 · outbound

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

Resolution: verified exact; local_arxiv, observed 2026-08-12T14:46:12.404161Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d6db856e-2210-47a3-80fb-49d46418c746 · outbound

This paper cites Torchcam: class activation explorer.

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

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:46:13.012369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 06da81c1-aa4c-44f6-928f-d2bdb49e03c8 · outbound

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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

Resolution: verified exact; doi, observed 2026-08-12T14:46:12.384963Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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

Resolution: unresolved; no resolver link, observed 2026-08-12T14:46:12.359134Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers