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Missingness Bias in Model Debugging

19 April 2022

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Papers citing "Missingness Bias in Model Debugging"

34 / 34 papers shown

Title
Probabilistic Stability Guarantees for Feature Attributions Helen Jin Anton Xue Weiqiu You Surbhi Goel Eric Wong 132 1 0 18 Apr 2025
Locality Alignment Improves Vision-Language Models Ian Covert Tony Sun James Zou Tatsunori Hashimoto VLM 245 6 0 14 Oct 2024
Explaining Black-box Model Predictions via Two-level Nested Feature Attributions with Consistency Property Yuya Yoshikawa Masanari Kimura Ryotaro Shimizu Yuki Saito FAtt 124 0 0 23 May 2024
Masked Autoencoders Are Scalable Vision Learners Kaiming He Xinlei Chen Saining Xie Yanghao Li Piotr Dollár Ross B. Girshick ViT TPM 477 7,819 0 11 Nov 2021
Certified Patch Robustness via Smoothed Vision Transformers Hadi Salman Saachi Jain Eric Wong Aleksander Mkadry AAML 106 59 0 11 Oct 2021
3DB: A Framework for Debugging Computer Vision Models Guillaume Leclerc Hadi Salman Andrew Ilyas Sai H. Vemprala Logan Engstrom ... Pengchuan Zhang Shibani Santurkar Greg Yang Ashish Kapoor Aleksander Madry 84 42 0 07 Jun 2021
Leveraging Sparse Linear Layers for Debuggable Deep Networks Eric Wong Shibani Santurkar Aleksander Madry FAtt 58 92 0 11 May 2021
Do Feature Attribution Methods Correctly Attribute Features? Yilun Zhou Serena Booth Marco Tulio Ribeiro J. Shah FAtt XAI 86 135 0 27 Apr 2021
Understanding Failures of Deep Networks via Robust Feature Extraction Sahil Singla Besmira Nushi S. Shah Ece Kamar Eric Horvitz FAtt 56 84 0 03 Dec 2020
Explaining by Removing: A Unified Framework for Model Explanation Ian Covert Scott M. Lundberg Su-In Lee FAtt 109 252 0 21 Nov 2020
An Analysis of LIME for Text Data Dina Mardaoui Damien Garreau FAtt 173 45 0 23 Oct 2020
Asymmetric Loss For Multi-Label Classification Emanuel Ben-Baruch T. Ridnik Nadav Zamir Asaf Noy Itamar Friedman M. Protter Lihi Zelnik-Manor 89 542 0 29 Sep 2020
Noise or Signal: The Role of Image Backgrounds in Object Recognition Kai Y. Xiao Logan Engstrom Andrew Ilyas Aleksander Madry 143 387 0 17 Jun 2020
On Completeness-aware Concept-Based Explanations in Deep Neural Networks Chih-Kuan Yeh Been Kim Sercan O. Arik Chun-Liang Li Tomas Pfister Pradeep Ravikumar FAtt 261 307 0 17 Oct 2019
Counterfactual Visual Explanations Yash Goyal Ziyan Wu Jan Ernst Dhruv Batra Devi Parikh Stefan Lee CML 80 512 0 16 Apr 2019
Sanity Checks for Saliency Maps Julius Adebayo Justin Gilmer M. Muelly Ian Goodfellow Moritz Hardt Been Kim FAtt AAML XAI 148 1,969 0 08 Oct 2018
Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations Dan Hendrycks Thomas G. Dietterich OOD 82 200 0 04 Jul 2018
A Benchmark for Interpretability Methods in Deep Neural Networks Sara Hooker D. Erhan Pieter-Jan Kindermans Been Kim FAtt UQCV 118 683 0 28 Jun 2018
Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives Amit Dhurandhar Pin-Yu Chen Ronny Luss Chun-Chen Tu Pai-Shun Ting Karthikeyan Shanmugam Payel Das FAtt 124 591 0 21 Feb 2018
How do Humans Understand Explanations from Machine Learning Systems? An Evaluation of the Human-Interpretability of Explanation Menaka Narayanan Emily Chen Jeffrey He Been Kim S. Gershman Finale Doshi-Velez FAtt XAI 104 244 0 02 Feb 2018
Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV) Been Kim Martin Wattenberg Justin Gilmer Carrie J. Cai James Wexler F. Viégas Rory Sayres FAtt 227 1,850 0 30 Nov 2017
The (Un)reliability of saliency methods Pieter-Jan Kindermans Sara Hooker Julius Adebayo Maximilian Alber Kristof T. Schütt Sven Dähne D. Erhan Been Kim FAtt XAI 106 688 0 02 Nov 2017
Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 317 12,131 0 19 Jun 2017
Real Time Image Saliency for Black Box Classifiers P. Dabkowski Y. Gal 70 592 0 22 May 2017
Network Dissection: Quantifying Interpretability of Deep Visual Representations David Bau Bolei Zhou A. Khosla A. Oliva Antonio Torralba MILM FAtt 158 1,523 1 19 Apr 2017
Interpretable Explanations of Black Boxes by Meaningful Perturbation Ruth C. Fong Andrea Vedaldi FAtt AAML 76 1,525 0 11 Apr 2017
Axiomatic Attribution for Deep Networks Mukund Sundararajan Ankur Taly Qiqi Yan OOD FAtt 193 6,018 0 04 Mar 2017
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 1.2K 17,033 0 16 Feb 2016
Rethinking the Inception Architecture for Computer Vision Christian Szegedy Vincent Vanhoucke Sergey Ioffe Jonathon Shlens Z. Wojna 3DV BDL 886 27,416 0 02 Dec 2015
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention Ke Xu Jimmy Ba Ryan Kiros Kyunghyun Cho Aaron Courville Ruslan Salakhutdinov R. Zemel Yoshua Bengio DiffM 348 10,079 0 10 Feb 2015
Explaining and Harnessing Adversarial Examples Ian Goodfellow Jonathon Shlens Christian Szegedy AAML GAN 282 19,121 0 20 Dec 2014
Intriguing properties of neural networks Christian Szegedy Wojciech Zaremba Ilya Sutskever Joan Bruna D. Erhan Ian Goodfellow Rob Fergus AAML 282 14,963 1 21 Dec 2013
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps Karen Simonyan Andrea Vedaldi Andrew Zisserman FAtt 314 7,316 0 20 Dec 2013
Visualizing and Understanding Convolutional Networks Matthew D. Zeiler Rob Fergus FAtt SSL 595 15,902 0 12 Nov 2013