Evaluating the visualization of what a Deep Neural Network has learned

21 September 2015

Papers citing "Evaluating the visualization of what a Deep Neural Network has learned"

50 / 511 papers shown

Title
Dual Decomposition of Convex Optimization Layers for Consistent Attention in Medical Images Tom Ron M. Weiler-Sagie Tamir Hazan FAtt MedIm 24 6 0 06 Jun 2022
Comparing interpretation methods in mental state decoding analyses with deep learning models A. Thomas Christopher Ré R. Poldrack AI4CE 18 2 0 31 May 2022
How explainable are adversarially-robust CNNs? Mehdi Nourelahi Lars Kotthoff Peijie Chen Anh Totti Nguyen AAML FAtt 22 8 0 25 May 2022
Deletion and Insertion Tests in Regression Models Naofumi Hama Masayoshi Mase Art B. Owen 27 8 0 25 May 2022
Towards Better Understanding Attribution Methods Sukrut Rao Moritz Bohle Bernt Schiele XAI 18 32 0 20 May 2022
The Solvability of Interpretability Evaluation Metrics Yilun Zhou J. Shah 70 8 0 18 May 2022
Explainable Deep Learning Methods in Medical Image Classification: A Survey Cristiano Patrício João C. Neves Luís F. Teixeira XAI 24 52 0 10 May 2022
Explain to Not Forget: Defending Against Catastrophic Forgetting with XAI Sami Ede Serop Baghdadlian Leander Weber A. Nguyen Dario Zanca Wojciech Samek Sebastian Lapuschkin CLL 27 6 0 04 May 2022
ExSum: From Local Explanations to Model Understanding Yilun Zhou Marco Tulio Ribeiro J. Shah FAtt LRM 19 25 0 30 Apr 2022
Missingness Bias in Model Debugging Saachi Jain Hadi Salman E. Wong Pengchuan Zhang Vibhav Vineet Sai H. Vemprala A. Madry 27 37 0 19 Apr 2022
Interpretability of Machine Learning Methods Applied to Neuroimaging Elina Thibeau-Sutre S. Collin Ninon Burgos O. Colliot 16 4 0 14 Apr 2022
Maximum Entropy Baseline for Integrated Gradients Hanxiao Tan FAtt 18 4 0 12 Apr 2022
Interpretable Research Replication Prediction via Variational Contextual Consistency Sentence Masking Tianyi Luo Rui Meng Qing Guo Y. Liu 12 4 0 28 Mar 2022
A Unified Study of Machine Learning Explanation Evaluation Metrics Yipei Wang Xiaoqian Wang XAI 19 7 0 27 Mar 2022
Towards Explainable Evaluation Metrics for Natural Language Generation Christoph Leiter Piyawat Lertvittayakumjorn M. Fomicheva Wei-Ye Zhao Yang Gao Steffen Eger AAML ELM 27 20 0 21 Mar 2022
Don't Get Me Wrong: How to Apply Deep Visual Interpretations to Time Series Christoffer Loeffler Wei-Cheng Lai Bjoern M. Eskofier Dario Zanca Lukas M. Schmidt Christopher Mutschler FAtt AI4TS 35 5 0 14 Mar 2022
Evaluating Explainable AI on a Multi-Modal Medical Imaging Task: Can Existing Algorithms Fulfill Clinical Requirements? Weina Jin Xiaoxiao Li Ghassan Hamarneh 27 51 0 12 Mar 2022
Fidelity of Interpretability Methods and Perturbation Artifacts in Neural Networks L. Brocki N. C. Chung AAML 16 4 0 06 Mar 2022
Do Explanations Explain? Model Knows Best Ashkan Khakzar Pedram J. Khorsandi Rozhin Nobahari Nassir Navab XAI AAML FAtt 11 23 0 04 Mar 2022
Threading the Needle of On and Off-Manifold Value Functions for Shapley Explanations Chih-Kuan Yeh Kuan-Yun Lee Frederick Liu Pradeep Ravikumar FAtt TDI 23 9 0 24 Feb 2022
Training Characteristic Functions with Reinforcement Learning: XAI-methods play Connect Four S. Wäldchen Felix Huber Sebastian Pokutta FAtt 28 8 0 23 Feb 2022
Evaluating Feature Attribution Methods in the Image Domain Arne Gevaert Axel-Jan Rousseau Thijs Becker D. Valkenborg T. D. Bie Yvan Saeys FAtt 24 22 0 22 Feb 2022
Guidelines and Evaluation of Clinical Explainable AI in Medical Image Analysis Weina Jin Xiaoxiao Li M. Fatehi Ghassan Hamarneh ELM XAI 42 88 0 16 Feb 2022
Don't Lie to Me! Robust and Efficient Explainability with Verified Perturbation Analysis Thomas Fel Mélanie Ducoffe David Vigouroux Rémi Cadène Mikael Capelle C. Nicodeme Thomas Serre AAML 26 41 0 15 Feb 2022
Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyond Anna Hedström Leander Weber Dilyara Bareeva Daniel G. Krakowczyk Franz Motzkus Wojciech Samek Sebastian Lapuschkin Marina M.-C. Höhne XAI ELM 21 168 0 14 Feb 2022
Measurably Stronger Explanation Reliability via Model Canonization Franz Motzkus Leander Weber Sebastian Lapuschkin FAtt 12 6 0 14 Feb 2022
InterpretTime: a new approach for the systematic evaluation of neural-network interpretability in time series classification Hugues Turbé Mina Bjelogrlic Christian Lovis G. Mengaldo AI4TS 22 6 0 11 Feb 2022
A Consistent and Efficient Evaluation Strategy for Attribution Methods Yao Rong Tobias Leemann V. Borisov Gjergji Kasneci Enkelejda Kasneci FAtt 23 92 0 01 Feb 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 16 12 0 28 Jan 2022
Model Agnostic Interpretability for Multiple Instance Learning Joseph Early C. Evers Sarvapali Ramchurn 11 11 0 27 Jan 2022
PREVIS -- A Combined Machine Learning and Visual Interpolation Approach for Interactive Reverse Engineering in Assembly Quality Control Patrick Ruediger-Flore Felix Claus V. Leonhardt H. Hagen J. Aurich Christoph Garth 19 0 0 25 Jan 2022
From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI Meike Nauta Jan Trienes Shreyasi Pathak Elisa Nguyen Michelle Peters Yasmin Schmitt Jorg Schlotterer M. V. Keulen C. Seifert ELM XAI 28 396 0 20 Jan 2022
Cyberbullying Classifiers are Sensitive to Model-Agnostic Perturbations Chris Emmery Ákos Kádár Grzegorz Chrupała Walter Daelemans 19 5 0 17 Jan 2022
Improving Deep Neural Network Classification Confidence using Heatmap-based eXplainable AI Erico Tjoa Hong Jing Khok Tushar Chouhan G. Cuntai FAtt 25 4 0 30 Dec 2021
Forward Composition Propagation for Explainable Neural Reasoning Isel Grau Gonzalo Nápoles M. Bello Yamisleydi Salgueiro A. Jastrzębska 22 0 0 23 Dec 2021
Explainable Artificial Intelligence Methods in Combating Pandemics: A Systematic Review F. Giuste Wenqi Shi Yuanda Zhu Tarun Naren Monica Isgut Ying Sha L. Tong Mitali S. Gupte May D. Wang 24 73 0 23 Dec 2021
More Than Words: Towards Better Quality Interpretations of Text Classifiers Muhammad Bilal Zafar Philipp Schmidt Michele Donini Cédric Archambeau F. Biessmann Sanjiv Ranjan Das K. Kenthapadi FAtt 12 5 0 23 Dec 2021
Toward Explainable AI for Regression Models S. Letzgus Patrick Wagner Jonas Lederer Wojciech Samek Klaus-Robert Muller G. Montavon XAI 30 63 0 21 Dec 2021
RELAX: Representation Learning Explainability Kristoffer Wickstrøm Daniel J. Trosten Sigurd Løkse Ahcène Boubekki Karl Øyvind Mikalsen Michael C. Kampffmeyer Robert Jenssen FAtt 13 14 0 19 Dec 2021
Global explainability in aligned image modalities Justin Engelmann Amos Storkey Miguel O. Bernabeu FAtt 30 4 0 17 Dec 2021
Evaluating saliency methods on artificial data with different background types Céline Budding Fabian Eitel K. Ritter Stefan Haufe XAI FAtt MedIm 27 5 0 09 Dec 2021
What I Cannot Predict, I Do Not Understand: A Human-Centered Evaluation Framework for Explainability Methods Julien Colin Thomas Fel Rémi Cadène Thomas Serre 33 101 0 06 Dec 2021
Improving Deep Learning Interpretability by Saliency Guided Training Aya Abdelsalam Ismail H. C. Bravo S. Feizi FAtt 20 80 0 29 Nov 2021
Fed2: Feature-Aligned Federated Learning Fuxun Yu Weishan Zhang Zhuwei Qin Zirui Xu Di Wang Chenchen Liu Zhi Tian Xiang Chen FedML 28 74 0 28 Nov 2021
Scrutinizing XAI using linear ground-truth data with suppressor variables Rick Wilming Céline Budding K. Müller Stefan Haufe FAtt 16 26 0 14 Nov 2021
A Robust Unsupervised Ensemble of Feature-Based Explanations using Restricted Boltzmann Machines V. Borisov Johannes Meier J. V. D. Heuvel Hamed Jalali Gjergji Kasneci FAtt 39 5 0 14 Nov 2021
Look at the Variance! Efficient Black-box Explanations with Sobol-based Sensitivity Analysis Thomas Fel Rémi Cadène Mathieu Chalvidal Matthieu Cord David Vigouroux Thomas Serre MLAU FAtt AAML 114 58 0 07 Nov 2021
Transparency of Deep Neural Networks for Medical Image Analysis: A Review of Interpretability Methods Zohaib Salahuddin Henry C. Woodruff A. Chatterjee Philippe Lambin 18 302 0 01 Nov 2021
Revisiting Sanity Checks for Saliency Maps G. Yona D. Greenfeld AAML FAtt 27 25 0 27 Oct 2021
Evaluating the Faithfulness of Importance Measures in NLP by Recursively Masking Allegedly Important Tokens and Retraining Andreas Madsen Nicholas Meade Vaibhav Adlakha Siva Reddy 111 35 0 15 Oct 2021