A Benchmark for Interpretability Methods in Deep Neural Networks

28 June 2018

Sara Hooker

D. Erhan

Pieter-Jan Kindermans

Papers citing "A Benchmark for Interpretability Methods in Deep Neural Networks"

50 / 142 papers shown

Title
From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection Moritz Vandenhirtz Julia E. Vogt 43 0 0 09 May 2025
PhysNav-DG: A Novel Adaptive Framework for Robust VLM-Sensor Fusion in Navigation Applications Trisanth Srinivasan Santosh Patapati 41 0 0 03 May 2025
Axiomatic Explainer Globalness via Optimal Transport Davin Hill Josh Bone A. Masoomi Max Torop Jennifer Dy 107 1 0 13 Mar 2025
FW-Shapley: Real-time Estimation of Weighted Shapley Values Pranoy Panda Siddharth Tandon V. Balasubramanian TDI 65 0 0 09 Mar 2025
Narrowing Information Bottleneck Theory for Multimodal Image-Text Representations Interpretability Zhiyu Zhu Zhibo Jin Jiayu Zhang Nan Yang Jiahao Huang Jianlong Zhou Fang Chen 46 0 0 16 Feb 2025
A Robust Adversarial Ensemble with Causal (Feature Interaction) Interpretations for Image Classification Chunheng Zhao P. Pisu G. Comert N. Begashaw Varghese Vaidyan Nina Christine Hubig AAML 37 0 0 31 Dec 2024
Unlearning-based Neural Interpretations Ching Lam Choi Alexandre Duplessis Serge Belongie FAtt 54 0 0 10 Oct 2024
F-Fidelity: A Robust Framework for Faithfulness Evaluation of Explainable AI Xu Zheng Farhad Shirani Zhuomin Chen Chaohao Lin Wei Cheng Wenbo Guo Dongsheng Luo AAML 38 0 0 03 Oct 2024
Explainable AI needs formal notions of explanation correctness Stefan Haufe Rick Wilming Benedict Clark Rustam Zhumagambetov Danny Panknin Ahcène Boubekki XAI 35 1 0 22 Sep 2024
Explainable AI for Autism Diagnosis: Identifying Critical Brain Regions Using fMRI Data Suryansh Vidya Kush Gupta Amir Aly Andy Wills Emmanuel Ifeachor Rohit Shankar 47 1 0 19 Sep 2024
Counterfactuals As a Means for Evaluating Faithfulness of Attribution Methods in Autoregressive Language Models Sepehr Kamahi Yadollah Yaghoobzadeh 55 0 0 21 Aug 2024
On the Evaluation Consistency of Attribution-based Explanations Jiarui Duan Haoling Li Haofei Zhang Hao Jiang Mengqi Xue Li Sun Mingli Song Mingli Song XAI 46 1 0 28 Jul 2024
Benchmarking the Attribution Quality of Vision Models Robin Hesse Simone Schaub-Meyer Stefan Roth FAtt 39 3 0 16 Jul 2024
Inpainting the Gaps: A Novel Framework for Evaluating Explanation Methods in Vision Transformers Lokesh Badisa Sumohana S. Channappayya 45 0 0 17 Jun 2024
Evaluating Saliency Explanations in NLP by Crowdsourcing Xiaotian Lu Jiyi Li Zhen Wan Xiaofeng Lin Koh Takeuchi Hisashi Kashima XAI FAtt LRM 34 1 0 17 May 2024
Data Science Principles for Interpretable and Explainable AI Kris Sankaran FaML 45 0 0 17 May 2024
Influence based explainability of brain tumors segmentation in multimodal Magnetic Resonance Imaging Tommaso Torda Andrea Ciardiello Simona Gargiulo Greta Grillo Simone Scardapane Cecilia Voena S. Giagu 29 0 0 05 Apr 2024
LeGrad: An Explainability Method for Vision Transformers via Feature Formation Sensitivity Walid Bousselham Angie Boggust Sofian Chaybouti Hendrik Strobelt Hilde Kuehne 96 10 0 04 Apr 2024
Accurate estimation of feature importance faithfulness for tree models Mateusz Gajewski Adam Karczmarz Mateusz Rapicki Piotr Sankowski 37 0 0 04 Apr 2024
Gradient based Feature Attribution in Explainable AI: A Technical Review Yongjie Wang Tong Zhang Xu Guo Zhiqi Shen XAI 29 19 0 15 Mar 2024
ALMANACS: A Simulatability Benchmark for Language Model Explainability Edmund Mills Shiye Su Stuart J. Russell Scott Emmons 56 7 0 20 Dec 2023
Variable Importance in High-Dimensional Settings Requires Grouping Ahmad Chamma Bertrand Thirion D. Engemann 49 4 0 18 Dec 2023
An adversarial attack approach for eXplainable AI evaluation on deepfake detection models Balachandar Gowrisankar V. Thing AAML 34 11 0 08 Dec 2023
Occlusion Sensitivity Analysis with Augmentation Subspace Perturbation in Deep Feature Space Pedro Valois Koichiro Niinuma Kazuhiro Fukui AAML 32 4 0 25 Nov 2023
Measuring and Improving Attentiveness to Partial Inputs with Counterfactuals Yanai Elazar Bhargavi Paranjape Hao Peng Sarah Wiegreffe Khyathi Raghavi Vivek Srikumar Sameer Singh Noah A. Smith AAML OOD 34 0 0 16 Nov 2023
SCAAT: Improving Neural Network Interpretability via Saliency Constrained Adaptive Adversarial Training Rui Xu Wenkang Qin Peixiang Huang Hao Wang Lin Luo FAtt AAML 40 2 0 09 Nov 2023
Advancing Post Hoc Case Based Explanation with Feature Highlighting Eoin M. Kenny Eoin Delaney Markt. Keane 36 5 0 06 Nov 2023
Intriguing Properties of Data Attribution on Diffusion Models Xiaosen Zheng Tianyu Pang Chao Du Jing Jiang Min Lin TDI 38 20 1 01 Nov 2023
Evaluating Explanation Methods for Vision-and-Language Navigation Guanqi Chen Lei Yang Guanhua Chen Jia Pan XAI 23 0 0 10 Oct 2023
Towards Best Practices of Activation Patching in Language Models: Metrics and Methods Fred Zhang Neel Nanda LLMSV 38 101 0 27 Sep 2023
Interpretability-Aware Vision Transformer Yao Qiang Chengyin Li Prashant Khanduri D. Zhu ViT 85 7 0 14 Sep 2023
FunnyBirds: A Synthetic Vision Dataset for a Part-Based Analysis of Explainable AI Methods Robin Hesse Simone Schaub-Meyer Stefan Roth AAML 37 33 0 11 Aug 2023
Precise Benchmarking of Explainable AI Attribution Methods Rafael Brandt Daan Raatjens G. Gaydadjiev XAI 27 4 0 06 Aug 2023
Discriminative Feature Attributions: Bridging Post Hoc Explainability and Inherent Interpretability Usha Bhalla Suraj Srinivas Himabindu Lakkaraju FAtt CML 36 6 0 27 Jul 2023
Uncovering Unique Concept Vectors through Latent Space Decomposition Mara Graziani Laura Mahony An-phi Nguyen Henning Muller Vincent Andrearczyk 43 4 0 13 Jul 2023
Can We Trust Explainable AI Methods on ASR? An Evaluation on Phoneme Recognition Xiao-lan Wu P. Bell A. Rajan 21 5 0 29 May 2023
A Review on Explainable Artificial Intelligence for Healthcare: Why, How, and When? M. Rubaiyat Hossain Mondal Prajoy Podder 28 56 0 10 Apr 2023
HarsanyiNet: Computing Accurate Shapley Values in a Single Forward Propagation Lu Chen Siyu Lou Keyan Zhang Jin Huang Quanshi Zhang TDI FAtt 29 9 0 04 Apr 2023
Don't be fooled: label leakage in explanation methods and the importance of their quantitative evaluation N. Jethani A. Saporta Rajesh Ranganath FAtt 31 11 0 24 Feb 2023
The Generalizability of Explanations Hanxiao Tan FAtt 18 1 0 23 Feb 2023
Less is More: The Influence of Pruning on the Explainability of CNNs David Weber F. Merkle Pascal Schöttle Stephan Schlögl Martin Nocker FAtt 34 1 0 17 Feb 2023
Towards a Deeper Understanding of Concept Bottleneck Models Through End-to-End Explanation Jack Furby Daniel Cunnington Dave Braines Alun D. Preece 22 6 0 07 Feb 2023
Negative Flux Aggregation to Estimate Feature Attributions X. Li Deng Pan Chengyin Li Yao Qiang D. Zhu FAtt 8 6 0 17 Jan 2023
Explaining Imitation Learning through Frames Boyuan Zheng Jianlong Zhou Chun-Hao Liu Yiqiao Li Fang Chen 14 0 0 03 Jan 2023
Comparing the Decision-Making Mechanisms by Transformers and CNNs via Explanation Methods Ming-Xiu Jiang Saeed Khorram Li Fuxin FAtt 27 9 0 13 Dec 2022
Identifying the Source of Vulnerability in Explanation Discrepancy: A Case Study in Neural Text Classification Ruixuan Tang Hanjie Chen Yangfeng Ji AAML FAtt 32 2 0 10 Dec 2022
Explaining Link Predictions in Knowledge Graph Embedding Models with Influential Examples Adrianna Janik Luca Costabello 19 2 0 05 Dec 2022
Attribution-based XAI Methods in Computer Vision: A Review Kumar Abhishek Deeksha Kamath 35 18 0 27 Nov 2022
On Pitfalls of Measuring Occlusion Robustness through Data Distortion Antonia Marcu 30 0 0 24 Nov 2022
ModelDiff: A Framework for Comparing Learning Algorithms Harshay Shah Sung Min Park Andrew Ilyas A. Madry SyDa 54 26 0 22 Nov 2022