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2011.05429
Cited By
Debugging Tests for Model Explanations
10 November 2020
Julius Adebayo
M. Muelly
Ilaria Liccardi
Been Kim
FAtt
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Papers citing
"Debugging Tests for Model Explanations"
50 / 135 papers shown
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259
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Informative Post-Hoc Explanations Only Exist for Simple Functions
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232
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219
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297
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Concept Probing: Where to Find Human-Defined Concepts (Extended Version)
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Afonso Leote
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306
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24 Jul 2025
Benchmarking Time-localized Explanations for Audio Classification Models
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Martin Meza
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335
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04 Jun 2025
Out-of-Distribution Detection via Channelwise Feature Aggregation in Neural Network-Based Receivers
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518
1
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21 May 2025
How to Achieve Higher Accuracy with Less Training Points?
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Anupam Pani
Yunchao Zhang
281
1
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Fast Fourier Correlation is a Highly Efficient and Accurate Feature Attribution Algorithm from the Perspective of Control Theory and Game Theory
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Feiyang Zhang
Wei Song
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Wei Wei
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483
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0
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Uncertainty Propagation in XAI: A Comparison of Analytical and Empirical Estimators
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Felix Bießmann
Frank Haußer
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Ekdeep Singh Lubana
Thomas Fel
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393
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Jacob S. Prince
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Victor Boutin
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Binxu Wang
Martin Wattenberg
Demba Ba
Talia Konkle
393
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Navigating the Maze of Explainable AI: A Systematic Approach to Evaluating Methods and Metrics
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Lukas Klein
Carsten T. Lüth
U. Schlegel
Till J. Bungert
Mennatallah El-Assady
Paul F. Jäger
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743
22
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Jayaram Raghuram
Shouqing Yang
Somesh Jha
346
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Lost in Context: The Influence of Context on Feature Attribution Methods for Object Recognition
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Sayanta Adhikari
Rishav Kumar
Konda Reddy Mopuri
Rajalakshmi Pachamuthu
274
1
0
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Feature Responsiveness Scores: Model-Agnostic Explanations for Recourse
International Conference on Learning Representations (ICLR), 2024
Seung Hyun Cheon
Anneke Wernerfelt
Sorelle A. Friedler
Berk Ustun
FaML
FAtt
684
8
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29 Oct 2024
Fool Me Once? Contrasting Textual and Visual Explanations in a Clinical Decision-Support Setting
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Maxime Kayser
Bayar I. Menzat
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Bogdan Bercean
Alex Novak
Abdala Espinosa
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Thomas Lukasiewicz
Oana-Maria Camburu
486
11
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AdaptGrad: Adaptive Sampling to Reduce Noise
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Chao Ma
Zepeng Wang
Libing Wu
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431
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10 Oct 2024
Joint Universal Adversarial Perturbations with Interpretations
Liang-bo Ning
Zeyu Dai
Wenqi Fan
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Luning Wang
Qing Li
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366
4
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03 Aug 2024
Comprehensive Attribution: Inherently Explainable Vision Model with Feature Detector
European Conference on Computer Vision (ECCV), 2024
Xianren Zhang
Dongwon Lee
Suhang Wang
VLM
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302
5
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27 Jul 2024
DEPICT: Diffusion-Enabled Permutation Importance for Image Classification Tasks
Sarah Jabbour
Gregory Kondas
Ella Kazerooni
Michael Sjoding
David Fouhey
Jenna Wiens
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286
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19 Jul 2024
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Junghoon Seo
Bomseok Park
Seongsu Lee
Junghyo Jo
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400
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Benchmarking the Attribution Quality of Vision Models
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Simone Schaub-Meyer
Stefan Roth
FAtt
381
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Regulating Model Reliance on Non-Robust Features by Smoothing Input Marginal Density
Peiyu Yang
Naveed Akhtar
Mubarak Shah
Lin Wang
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279
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05 Jul 2024
Axiomatization of Gradient Smoothing in Neural Networks
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Xiaochuan Shi
Chao Ma
Zepeng Wang
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265
1
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Provably Better Explanations with Optimized Aggregation of Feature Attributions
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Thomas Decker
Ananta R. Bhattarai
Jindong Gu
Volker Tresp
Florian Buettner
242
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Allowing humans to interactively guide machines where to look does not always improve human-AI team's classification accuracy
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Mohammad Reza Taesiri
Sunnie S. Y. Kim
Anh Totti Nguyen
HAI
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536
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Romy Müller
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286
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What Sketch Explainability Really Means for Downstream Tasks
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Pinaki Nath Chowdhury
A. Bhunia
Aneeshan Sain
Tao Xiang
Yi-Zhe Song
387
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H. Nguyen
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Isao Echizen
247
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13 Feb 2024
Black-Box Access is Insufficient for Rigorous AI Audits
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Stephen Casper
Carson Ezell
Charlotte Siegmann
Noam Kolt
Taylor Lynn Curtis
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David Bau
Max Tegmark
David M. Krueger
Dylan Hadfield-Menell
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720
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Fast Diffusion-Based Counterfactuals for Shortcut Removal and Generation
Nina Weng
Paraskevas Pegios
Eike Petersen
Aasa Feragen
Siavash Bigdeli
MedIm
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442
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21 Dec 2023
Evaluating the Utility of Model Explanations for Model Development
Shawn Im
Jacob Andreas
Yilun Zhou
XAI
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360
2
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10 Dec 2023
DiG-IN: Diffusion Guidance for Investigating Networks -- Uncovering Classifier Differences Neuron Visualisations and Visual Counterfactual Explanations
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Maximilian Augustin
Yannic Neuhaus
Matthias Hein
DiffM
416
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On the Feasibility of Reasoning about the Internal States of Blackbox IoT Devices Using Side-Channel Information
Wei Sun
Yuwei Xiao
Haojian Jin
Dinesh Bharadia
144
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(Why) Is My Prompt Getting Worse? Rethinking Regression Testing for Evolving LLM APIs
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Jane Hsieh
222
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337
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Chirag Shah
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Walter J. Scheirer
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306
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Can Large Language Models Explain Themselves? A Study of LLM-Generated Self-Explanations
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May I Ask a Follow-up Question? Understanding the Benefits of Conversations in Neural Network Explainability
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335
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227
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Good-looking but Lacking Faithfulness: Understanding Local Explanation Methods through Trend-based Testing
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Kai Chen
Guozhu Meng
Jiangshan Zhang
Congyi Li
FAtt
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345
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Stefan Roth
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352
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