Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications

17 March 2020

Wojciech Samek

G. Montavon

Sebastian Lapuschkin

Christopher J. Anders

K. Müller

XAI

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Papers citing "Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications"

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Title
Reimagining Anomalies: What If Anomalies Were Normal? Philipp Liznerski Saurabh Varshneya Ece Calikus Sophie Fellenz Marius Kloft 33 4 0 22 Feb 2024
Towards Early Prediction of Human iPSC Reprogramming Success Abhineet Singh I. Jasra Omar Mouhammed N. Dadheech Nilanjan Ray J. Shapiro 28 0 0 23 May 2023
Artificial intelligence to advance Earth observation: a perspective D. Tuia Konrad Schindler Begum Demir Gustau Camps-Valls Xiao Xiang Zhu ... Mihai Datcu Jorge-Arnulfo Quiané-Ruiz Volker Markl Bertrand Le Saux Rochelle Schneider 31 10 0 15 May 2023
Understanding and Unifying Fourteen Attribution Methods with Taylor Interactions Huiqi Deng Na Zou Mengnan Du Weifu Chen Guo-Can Feng Ziwei Yang Zheyang Li Quanshi Zhang FAtt TDI 49 22 0 02 Mar 2023
Extractive Text Summarization Using Generalized Additive Models with Interactions for Sentence Selection Vinícius Camargo Da Silva João Paulo Papa K. Costa 16 1 0 21 Dec 2022
Explainable Analysis of Deep Learning Methods for SAR Image Classification Sheng Su Ziteng Cui Weiwei Guo Zenghui Zhang Wenxian Yu XAI 25 12 0 14 Apr 2022
Feature Visualization within an Automated Design Assessment leveraging Explainable Artificial Intelligence Methods Raoul Schönhof Artem Werner J. Elstner Boldizsar Zopcsak Ramez Awad Marco F. Huber AAML 8 12 0 28 Jan 2022
Negative Evidence Matters in Interpretable Histology Image Classification Soufiane Belharbi M. Pedersoli Ismail Ben Ayed Luke McCaffrey Eric Granger 18 11 0 07 Jan 2022
Deep Learning and Earth Observation to Support the Sustainable Development Goals Claudio Persello Jan Dirk Wegner Ronny Hansch D. Tuia Pedram Ghamisi M. Koeva Gustau Camps-Valls 26 4 0 21 Dec 2021
Evaluation of Interpretability for Deep Learning algorithms in EEG Emotion Recognition: A case study in Autism J. M. M. Torres Sara E. Medina-DeVilliers T. Clarkson M. Lerner Giuseppe Riccardi 17 34 0 25 Nov 2021
KML: Using Machine Learning to Improve Storage Systems I. Akgun A. S. Aydin Andrew Burford Michael McNeill Michael Arkhangelskiy Aadil Shaikh L. Velikov E. Zadok 11 1 0 22 Nov 2021
TorchEsegeta: Framework for Interpretability and Explainability of Image-based Deep Learning Models S. Chatterjee Arnab Das Chirag Mandal Budhaditya Mukhopadhyay Manish Vipinraj Aniruddh Shukla R. Rao Chompunuch Sarasaen Oliver Speck A. Nürnberger MedIm 26 14 0 16 Oct 2021
Logic Explained Networks Gabriele Ciravegna Pietro Barbiero Francesco Giannini Marco Gori Pietro Lió Marco Maggini S. Melacci 25 69 0 11 Aug 2021
Entropy-based Logic Explanations of Neural Networks Pietro Barbiero Gabriele Ciravegna Francesco Giannini Pietro Lió Marco Gori S. Melacci FAtt XAI 21 78 0 12 Jun 2021
Understanding Neural Code Intelligence Through Program Simplification Md Rafiqul Islam Rabin Vincent J. Hellendoorn Mohammad Amin Alipour AAML 41 58 0 07 Jun 2021
A General Taylor Framework for Unifying and Revisiting Attribution Methods Huiqi Deng Na Zou Mengnan Du Weifu Chen Guo-Can Feng Xia Hu TDI FAtt 31 2 0 28 May 2021
A Comprehensive Taxonomy for Explainable Artificial Intelligence: A Systematic Survey of Surveys on Methods and Concepts Gesina Schwalbe Bettina Finzel XAI 21 184 0 15 May 2021
Mutual Information Preserving Back-propagation: Learn to Invert for Faithful Attribution Huiqi Deng Na Zou Weifu Chen Guo-Can Feng Mengnan Du Xia Hu FAtt 18 6 0 14 Apr 2021
Towards a Collective Agenda on AI for Earth Science Data Analysis D. Tuia R. Roscher Jan Dirk Wegner Nathan Jacobs Xiaoxiang Zhu Gustau Camps-Valls AI4CE 39 68 0 11 Apr 2021
Evaluating explainable artificial intelligence methods for multi-label deep learning classification tasks in remote sensing Ioannis Kakogeorgiou Konstantinos Karantzalos XAI 23 118 0 03 Apr 2021
Towards a mathematical framework to inform Neural Network modelling via Polynomial Regression Pablo Morala Jenny Alexandra Cifuentes R. Lillo Iñaki Ucar 17 34 0 07 Feb 2021
Towards Robust Explanations for Deep Neural Networks Ann-Kathrin Dombrowski Christopher J. Anders K. Müller Pan Kessel FAtt 13 62 0 18 Dec 2020
Deep Interpretable Classification and Weakly-Supervised Segmentation of Histology Images via Max-Min Uncertainty Soufiane Belharbi Jérôme Rony Jose Dolz Ismail Ben Ayed Luke McCaffrey Eric Granger 6 52 0 14 Nov 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 34 7 0 23 Oct 2020
Geometric Disentanglement by Random Convex Polytopes M. Joswig M. Kaluba Lukas Ruff 17 3 0 29 Sep 2020
A Unifying Review of Deep and Shallow Anomaly Detection Lukas Ruff Jacob R. Kauffmann Robert A. Vandermeulen G. Montavon Wojciech Samek Marius Kloft Thomas G. Dietterich Klaus-Robert Muller UQCV 18 779 0 24 Sep 2020
MeLIME: Meaningful Local Explanation for Machine Learning Models T. Botari Frederik Hvilshoj Rafael Izbicki A. Carvalho AAML FAtt 20 16 0 12 Sep 2020
Langevin Cooling for Domain Translation Vignesh Srinivasan Klaus-Robert Muller Wojciech Samek Shinichi Nakajima 36 1 0 31 Aug 2020
A Unified Taylor Framework for Revisiting Attribution Methods Huiqi Deng Na Zou Mengnan Du Weifu Chen Guo-Can Feng Xia Hu FAtt TDI 27 21 0 21 Aug 2020
Explainable Deep One-Class Classification Philipp Liznerski Lukas Ruff Robert A. Vandermeulen Billy Joe Franks Marius Kloft Klaus-Robert Muller 13 198 0 03 Jul 2020
How Much Can I Trust You? -- Quantifying Uncertainties in Explaining Neural Networks Kirill Bykov Marina M.-C. Höhne Klaus-Robert Muller Shinichi Nakajima Marius Kloft UQCV FAtt 16 31 0 16 Jun 2020
Rethinking Assumptions in Deep Anomaly Detection Lukas Ruff Robert A. Vandermeulen Billy Joe Franks Klaus-Robert Muller Marius Kloft 14 89 0 30 May 2020
On the Explanation of Machine Learning Predictions in Clinical Gait Analysis D. Slijepcevic Fabian Horst Sebastian Lapuschkin Anna-Maria Raberger Matthias Zeppelzauer Wojciech Samek C. Breiteneder W. Schöllhorn B. Horsak 22 50 0 16 Dec 2019
Physically Interpretable Neural Networks for the Geosciences: Applications to Earth System Variability B. Toms E. Barnes I. Ebert‐Uphoff AI4CE 23 213 0 04 Dec 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 23 70 0 08 Sep 2019
Methods for Interpreting and Understanding Deep Neural Networks G. Montavon Wojciech Samek K. Müller FaML 234 2,235 0 24 Jun 2017
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks Guy Katz Clark W. Barrett D. Dill Kyle D. Julian Mykel Kochenderfer AAML 226 1,835 0 03 Feb 2017
Deep image mining for diabetic retinopathy screening G. Quellec K. Charrière Yassine Boudi B. Cochener M. Lamard MedIm 34 413 0 22 Oct 2016
Classifying and Segmenting Microscopy Images Using Convolutional Multiple Instance Learning Oren Z. Kraus Lei Jimmy Ba B. Frey 161 392 0 17 Nov 2015