The (Un)reliability of saliency methods

2 November 2017

Pieter-Jan Kindermans

Papers citing "The (Un)reliability of saliency methods"

48 / 148 papers shown

Title
EXplainable Neural-Symbolic Learning (X-NeSyL) methodology to fuse deep learning representations with expert knowledge graphs: the MonuMAI cultural heritage use case Natalia Díaz Rodríguez Alberto Lamas Jules Sanchez Gianni Franchi Ivan Donadello Siham Tabik David Filliat P. Cruz Rosana Montes Francisco Herrera 49 77 0 24 Apr 2021
On the Sensitivity and Stability of Model Interpretations in NLP Fan Yin Zhouxing Shi Cho-Jui Hsieh Kai-Wei Chang FAtt 19 33 0 18 Apr 2021
Neural Network Attribution Methods for Problems in Geoscience: A Novel Synthetic Benchmark Dataset Antonios Mamalakis I. Ebert‐Uphoff E. Barnes OOD 28 75 0 18 Mar 2021
Do Input Gradients Highlight Discriminative Features? Harshay Shah Prateek Jain Praneeth Netrapalli AAML FAtt 23 57 0 25 Feb 2021
Connecting Interpretability and Robustness in Decision Trees through Separation Michal Moshkovitz Yao-Yuan Yang Kamalika Chaudhuri 33 22 0 14 Feb 2021
Towards Robust Explanations for Deep Neural Networks Ann-Kathrin Dombrowski Christopher J. Anders K. Müller Pan Kessel FAtt 24 63 0 18 Dec 2020
Neural Prototype Trees for Interpretable Fine-grained Image Recognition Meike Nauta Ron van Bree C. Seifert 80 262 0 03 Dec 2020
Reflective-Net: Learning from Explanations Johannes Schneider Michalis Vlachos FAtt OffRL LRM 57 18 0 27 Nov 2020
Now You See Me (CME): Concept-based Model Extraction Dmitry Kazhdan B. Dimanov M. Jamnik Pietro Lió Adrian Weller 25 72 0 25 Oct 2020
Exemplary Natural Images Explain CNN Activations Better than State-of-the-Art Feature Visualization Judy Borowski Roland S. Zimmermann Judith Schepers Robert Geirhos Thomas S. A. Wallis Matthias Bethge Wieland Brendel FAtt 39 7 0 23 Oct 2020
Optimism in the Face of Adversity: Understanding and Improving Deep Learning through Adversarial Robustness Guillermo Ortiz-Jiménez Apostolos Modas Seyed-Mohsen Moosavi-Dezfooli P. Frossard AAML 29 48 0 19 Oct 2020
The elephant in the interpretability room: Why use attention as explanation when we have saliency methods? Jasmijn Bastings Katja Filippova XAI LRM 46 173 0 12 Oct 2020
What Do You See? Evaluation of Explainable Artificial Intelligence (XAI) Interpretability through Neural Backdoors Yi-Shan Lin Wen-Chuan Lee Z. Berkay Celik XAI 29 93 0 22 Sep 2020
A simple defense against adversarial attacks on heatmap explanations Laura Rieger Lars Kai Hansen FAtt AAML 33 37 0 13 Jul 2020
Usefulness of interpretability methods to explain deep learning based plant stress phenotyping Koushik Nagasubramanian Asheesh K. Singh Arti Singh S. Sarkar Baskar Ganapathysubramanian FAtt 19 16 0 11 Jul 2020
Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey Arun Das P. Rad XAI 27 593 0 16 Jun 2020
Evolved Explainable Classifications for Lymph Node Metastases Iam Palatnik de Sousa M. Vellasco E. C. Silva 19 6 0 14 May 2020
Evaluating and Aggregating Feature-based Model Explanations Umang Bhatt Adrian Weller J. M. F. Moura XAI 33 218 0 01 May 2020
Explainable Deep Learning: A Field Guide for the Uninitiated Gabrielle Ras Ning Xie Marcel van Gerven Derek Doran AAML XAI 41 371 0 30 Apr 2020
A Survey of Deep Learning for Scientific Discovery M. Raghu Erica Schmidt OOD AI4CE 38 120 0 26 Mar 2020
Measuring and improving the quality of visual explanations Agnieszka Grabska-Barwiñska XAI FAtt 21 3 0 14 Mar 2020
Explaining Explanations: Axiomatic Feature Interactions for Deep Networks Joseph D. Janizek Pascal Sturmfels Su-In Lee FAtt 30 143 0 10 Feb 2020
SAUNet: Shape Attentive U-Net for Interpretable Medical Image Segmentation Jesse Sun Fatemeh Darbeha M. Zaidi Bo Wang AAML 19 110 0 21 Jan 2020
CXPlain: Causal Explanations for Model Interpretation under Uncertainty Patrick Schwab W. Karlen FAtt CML 34 205 0 27 Oct 2019
Seeing What a GAN Cannot Generate David Bau Jun-Yan Zhu Jonas Wulff William S. Peebles Hendrik Strobelt Bolei Zhou Antonio Torralba GAN 48 308 0 24 Oct 2019
Semantics for Global and Local Interpretation of Deep Neural Networks Jindong Gu Volker Tresp AI4CE 27 14 0 21 Oct 2019
Deep Weakly-Supervised Learning Methods for Classification and Localization in Histology Images: A Survey Jérôme Rony Soufiane Belharbi Jose Dolz Ismail Ben Ayed Luke McCaffrey Eric Granger 25 70 0 08 Sep 2019
Saccader: Improving Accuracy of Hard Attention Models for Vision Gamaleldin F. Elsayed Simon Kornblith Quoc V. Le VLM 29 71 0 20 Aug 2019
Visual Interaction with Deep Learning Models through Collaborative Semantic Inference Sebastian Gehrmann Hendrik Strobelt Robert Krüger Hanspeter Pfister Alexander M. Rush HAI 21 57 0 24 Jul 2019
Incorporating Priors with Feature Attribution on Text Classification Frederick Liu Besim Avci FAtt FaML 31 120 0 19 Jun 2019
Adversarial Robustness as a Prior for Learned Representations Logan Engstrom Andrew Ilyas Shibani Santurkar Dimitris Tsipras Brandon Tran A. Madry OOD AAML 24 63 0 03 Jun 2019
Certifiably Robust Interpretation in Deep Learning Alexander Levine Sahil Singla S. Feizi FAtt AAML 23 63 0 28 May 2019
What Do Adversarially Robust Models Look At? Takahiro Itazuri Yoshihiro Fukuhara Hirokatsu Kataoka Shigeo Morishima 19 5 0 19 May 2019
Detecting inter-sectional accuracy differences in driver drowsiness detection algorithms Mkhuseli Ngxande J. Tapamo Michael G. Burke 22 12 0 23 Apr 2019
Software and application patterns for explanation methods Maximilian Alber 33 11 0 09 Apr 2019
Regression Concept Vectors for Bidirectional Explanations in Histopathology Mara Graziani Vincent Andrearczyk Henning Muller 39 78 0 09 Apr 2019
Explaining Anomalies Detected by Autoencoders Using SHAP Liat Antwarg Ronnie Mindlin Miller Bracha Shapira Lior Rokach FAtt TDI 19 86 0 06 Mar 2019
Understanding Impacts of High-Order Loss Approximations and Features in Deep Learning Interpretation Sahil Singla Eric Wallace Shi Feng S. Feizi FAtt 18 59 0 01 Feb 2019
Interpretable Deep Learning under Fire Xinyang Zhang Ningfei Wang Hua Shen S. Ji Xiapu Luo Ting Wang AAML AI4CE 22 169 0 03 Dec 2018
An Overview of Computational Approaches for Interpretation Analysis Philipp Blandfort Jörn Hees D. Patton 21 2 0 09 Nov 2018
What made you do this? Understanding black-box decisions with sufficient input subsets Brandon Carter Jonas W. Mueller Siddhartha Jain David K Gifford FAtt 37 77 0 09 Oct 2018
Sanity Checks for Saliency Maps Julius Adebayo Justin Gilmer M. Muelly Ian Goodfellow Moritz Hardt Been Kim FAtt AAML XAI 64 1,928 0 08 Oct 2018
xGEMs: Generating Examplars to Explain Black-Box Models Shalmali Joshi Oluwasanmi Koyejo Been Kim Joydeep Ghosh MLAU 25 40 0 22 Jun 2018
On the Robustness of Interpretability Methods David Alvarez-Melis Tommi Jaakkola 30 522 0 21 Jun 2018
Towards Robust Interpretability with Self-Explaining Neural Networks David Alvarez-Melis Tommi Jaakkola MILM XAI 56 933 0 20 Jun 2018
Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges Gabrielle Ras Marcel van Gerven W. Haselager XAI 17 217 0 20 Mar 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 77 1,791 0 30 Nov 2017
Deep Learning Techniques for Music Generation -- A Survey Jean-Pierre Briot Gaëtan Hadjeres F. Pachet MGen 34 297 0 05 Sep 2017