Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-05T16:39:26.477845Z
Paper Citation Record · LEDGER
Assessing the Noise Robustness of Class Activation Maps: A Framework for Reliable Model Interpretability
As of 15 August 2026, this Paper Citation Record lists 55 of 55 outbound references and 0 inbound Pith citation observations for arXiv:2508.18154.
A citation records a reference. It does not transfer a finding from one paper to another.
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Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
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Source: cited_works
Reference resolution
55 of 55 outbound references displayed
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External citation measurements
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Outbound references
Observation d43bf82b-8903-4dd3-b57b-27b32e1b7862 · outbound
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,
Reference 1
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Observation 07db3934-68ca-4ba9-8ca1-54e5721305f0 · outbound
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,
Reference 2
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Observation a5ee61b9-0b13-486e-bd71-80f4f60d0dd1 · outbound
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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Observation 26bd82ab-17f0-47e8-96ad-c6613c1a9f56 · outbound
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,
Reference 4
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Observation 4aada800-0525-43a9-bb53-840d700c7def · outbound
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,
Reference 5
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Observation 8e8cd51e-4da0-43de-8c6c-139761b5805f · outbound
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,
Reference 6
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Observation b5d539e4-8d47-422e-ae27-c768c81ec6a9 · outbound
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,
Reference 7
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Observation 0ef6ddc7-135b-44c0-b978-be7f3aecd57a · outbound
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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Observation 2ec214b5-08cd-4b62-8c24-629b649d7a93 · outbound
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,
Reference 9
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Observation bde2ddb3-ff79-42a0-94ce-63de68822e72 · outbound
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
Reference 10
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Observation 7b66cab5-f843-4d29-b815-b585df15d6e6 · outbound
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,
Reference 11
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Observation c4e53f9a-85c3-41ae-8b6a-b69d58cb910e · outbound
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,
Reference 12
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Observation a86b83c6-7c9c-4f9d-997a-7619fec4b477 · outbound
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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Observation f399a31c-93a1-48ef-a6e1-f22498ed63c1 · outbound
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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Observation 285c6a11-9fce-418f-9337-767c350d2442 · outbound
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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Observation 8c7ac2a5-3e10-4e0b-8d10-467b4b2cf4ee · outbound
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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Source-reported events for the cited work
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Observation 457dc22e-91ff-4b12-ac4f-5b403c204e5b · outbound
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,
Reference 17
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Observation db336035-5263-48d4-8c7f-85692fc83e38 · outbound
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,
Reference 18
- Resolution
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Source-reported events for the cited work
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Observation 820c20a2-eb7a-4fae-9540-acb36e695f1e · outbound
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,
Reference 19
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Observation ef09b02f-5179-447e-a8af-e2dd34b5ca7c · outbound
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,
Reference 20
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Observation 72af8c86-4eb8-46bd-afad-0b92230984a3 · outbound
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
- Resolution
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Observation ee66c359-7c5d-41d9-a884-382c3fc229e3 · outbound
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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Source-reported events for the cited work
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Observation a116f8bd-a446-40eb-b0f7-b8e62b0de11d · outbound
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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Source-reported events for the cited work
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Observation a8668563-6638-4b87-81dc-8a2bca36a907 · outbound
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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Observation 5f6ab8e1-f9cc-4ef6-a2d2-30566b4d32a7 · outbound
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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Observation 02f5a4c4-9c18-4e88-b942-6f2340a37acf · outbound
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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Observation 2aa9e4c9-268a-4297-b08b-b65b800a2a4d · outbound
This paper cites How explainable are adversarially-robust CNNs?.
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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Observation 4258409e-8781-463a-8871-e89065ca85d8 · outbound
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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Observation dd6644f0-01d9-4662-b83b-3ed7ef53bdf3 · outbound
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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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Observation 10ed37be-e2ca-4f7f-a618-196767430fc3 · outbound
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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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Observation 95f1f24c-7b0e-4520-8bf5-8757b37b4f7c · outbound
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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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Observation 2f645eb9-cc15-45b2-a4a7-9e8776a9a912 · outbound
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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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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Observation 246cf2fd-4b14-4762-b648-6f606ccec284 · outbound
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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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Observation 6691e190-b6ad-467c-a578-d3081d5ae69f · outbound
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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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Observation 888c4342-3fc2-466a-a62e-d1ee9b73864e · outbound
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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Observation 09e7afd3-d6d2-43a9-80ae-586e6558a48d · outbound
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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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Observation 901ed065-0a10-4950-af69-a514bd7b1b2b · outbound
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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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 1a3c6db8-59d2-4b90-9757-69cac73d0e59 · outbound
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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Source-reported events for the cited work
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Observation 6f2fc901-2bd5-46d3-8065-92c64bad92a1 · outbound
This paper cites Icev2: Interpretability, comprehensiveness, and explainability in vision transformer,.
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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Observation 0f8dd145-116d-4c4b-8673-fcbd54508320 · outbound
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Assessing the Noise Robustness of Class Activation Maps: A Framework for Reliable Model Interpretability JPEG Compression Standard,
Reference 43
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Source-reported events for the cited work
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Observation c9739249-808e-47ed-b910-8b51f0b7346c · outbound
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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Source-reported events for the cited work
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Observation abb4a957-b7ca-4e75-9158-21d8f58073f2 · outbound
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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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Source-reported events for the cited work
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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
Reference 46
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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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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 637a91b9-d14b-4a76-bfca-de0cbe1f5c25 · outbound
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T16:39:26.894445Z
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 52f4ae85-4c9d-4a79-9710-a719c80c88fc · outbound
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T16:39:26.872434Z
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 f59d7782-c3b5-406f-89ab-dbd8d9dd0846 · outbound
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
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T16:39:26.450906Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3c3325bc-0874-490e-8fe8-4012fe4f9be6 · outbound
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,
Reference 51
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T16:39:26.836460Z
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 933c4c9c-9300-421b-a002-f4ac55ea490c · outbound
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,
Reference 52
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T16:39:26.815912Z
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 9fc931a0-7b6d-44b6-8039-5e4ee67ccf35 · outbound
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,
Reference 53
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T16:39:26.795115Z
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 1f177771-355a-463f-91ee-4150a4d52d3c · outbound
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,
Reference 54
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T16:39:26.770933Z
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 717dccbe-2b36-413a-aeaf-d585a96f5be0 · outbound
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,
Reference 55
- Resolution
- malformed identifier
- raw_fallback, observed 2026-08-05T16:39:26.751962Z
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.
Pith citing papers
No inbound Pith citation observations are available.