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 / 510 papers shown

Title
Survey of XAI in digital pathology Milda Pocevičiūtė Gabriel Eilertsen Claes Lundström 8 56 0 14 Aug 2020
ExplAIn: Explanatory Artificial Intelligence for Diabetic Retinopathy Diagnosis G. Quellec Hassan Al Hajj M. Lamard Pierre-Henri Conze P. Massin B. Cochener 25 54 0 13 Aug 2020
More Than Accuracy: Towards Trustworthy Machine Learning Interfaces for Object Recognition Hendrik Heuer Andreas Breiter HAI 17 9 0 05 Aug 2020
Explainable Face Recognition Jonathan R. Williford Brandon B. May J. Byrne CVBM 16 71 0 03 Aug 2020
The role of explainability in creating trustworthy artificial intelligence for health care: a comprehensive survey of the terminology, design choices, and evaluation strategies A. Markus J. Kors P. Rijnbeek 15 455 0 31 Jul 2020
Split and Expand: An inference-time improvement for Weakly Supervised Cell Instance Segmentation Lin Geng Foo Rui En Ho Jiamei Sun Alexander Binder 13 0 0 21 Jul 2020
Fairwashing Explanations with Off-Manifold Detergent Christopher J. Anders Plamen Pasliev Ann-Kathrin Dombrowski K. Müller Pan Kessel FAtt FaML 16 94 0 20 Jul 2020
Technologies for Trustworthy Machine Learning: A Survey in a Socio-Technical Context Ehsan Toreini Mhairi Aitken Kovila P. L. Coopamootoo Karen Elliott Vladimiro González-Zelaya P. Missier Magdalene Ng Aad van Moorsel 31 17 0 17 Jul 2020
Evaluation for Weakly Supervised Object Localization: Protocol, Metrics, and Datasets Junsuk Choe Seong Joon Oh Sanghyuk Chun Seungho Lee Zeynep Akata Hyunjung Shim WSOL 336 23 0 08 Jul 2020
Solving the Order Batching and Sequencing Problem using Deep Reinforcement Learning Bram Cals Yingqian Zhang R. Dijkman Claudy van Dorst OffRL 9 29 0 16 Jun 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 27 31 0 16 Jun 2020
Higher-Order Explanations of Graph Neural Networks via Relevant Walks Thomas Schnake Oliver Eberle Jonas Lederer Shinichi Nakajima Kristof T. Schütt Klaus-Robert Muller G. Montavon 32 215 0 05 Jun 2020
Black-box Explanation of Object Detectors via Saliency Maps Vitali Petsiuk R. Jain Varun Manjunatha Vlad I. Morariu Ashutosh Mehra Vicente Ordonez Kate Saenko FAtt 14 122 0 05 Jun 2020
Evaluations and Methods for Explanation through Robustness Analysis Cheng-Yu Hsieh Chih-Kuan Yeh Xuanqing Liu Pradeep Ravikumar Seungyeon Kim Sanjiv Kumar Cho-Jui Hsieh XAI 15 58 0 31 May 2020
Explainable Artificial Intelligence: a Systematic Review Giulia Vilone Luca Longo XAI 20 266 0 29 May 2020
Large scale evaluation of importance maps in automatic speech recognition V. Trinh Michael I. Mandel 8 4 0 21 May 2020
Towards explainable classifiers using the counterfactual approach -- global explanations for discovering bias in data Agnieszka Mikołajczyk M. Grochowski Arkadiusz Kwasigroch FAtt CML 12 3 0 05 May 2020
Evaluating and Aggregating Feature-based Model Explanations Umang Bhatt Adrian Weller J. M. F. Moura XAI 33 218 0 01 May 2020
Towards Visually Explaining Video Understanding Networks with Perturbation Zhenqiang Li Weimin Wang Zuoyue Li Yifei Huang Yoichi Sato FAtt 20 3 0 01 May 2020
Explainable Deep Learning: A Field Guide for the Uninitiated Gabrielle Ras Ning Xie Marcel van Gerven Derek Doran AAML XAI 38 370 0 30 Apr 2020
Assessing the Reliability of Visual Explanations of Deep Models with Adversarial Perturbations Dan Valle Tiago Pimentel Adriano Veloso FAtt XAI AAML 20 3 0 22 Apr 2020
Understanding Integrated Gradients with SmoothTaylor for Deep Neural Network Attribution Gary S. W. Goh Sebastian Lapuschkin Leander Weber Wojciech Samek Alexander Binder FAtt 6 34 0 22 Apr 2020
Approximate Inverse Reinforcement Learning from Vision-based Imitation Learning Keuntaek Lee Bogdan I. Vlahov Jason Gibson James M. Rehg Evangelos A. Theodorou 6 10 0 17 Apr 2020
DeepStreamCE: A Streaming Approach to Concept Evolution Detection in Deep Neural Networks Lorraine Chambers M. Gaber Z. Abdallah 21 4 0 08 Apr 2020
Generating Hierarchical Explanations on Text Classification via Feature Interaction Detection Hanjie Chen Guangtao Zheng Yangfeng Ji FAtt 36 91 0 04 Apr 2020
Continual Learning with Node-Importance based Adaptive Group Sparse Regularization Sangwon Jung Hongjoon Ahn Sungmin Cha Taesup Moon CLL 17 120 0 30 Mar 2020
Layerwise Knowledge Extraction from Deep Convolutional Networks S. Odense Artur Garcez FAtt 16 9 0 19 Mar 2020
Overinterpretation reveals image classification model pathologies Brandon Carter Siddhartha Jain Jonas W. Mueller David K Gifford FAtt 20 50 0 19 Mar 2020
Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications Wojciech Samek G. Montavon Sebastian Lapuschkin Christopher J. Anders K. Müller XAI 44 82 0 17 Mar 2020
Ground Truth Evaluation of Neural Network Explanations with CLEVR-XAI L. Arras Ahmed Osman Wojciech Samek XAI AAML 21 150 0 16 Mar 2020
GAMI-Net: An Explainable Neural Network based on Generalized Additive Models with Structured Interactions Zebin Yang Aijun Zhang Agus Sudjianto FAtt 19 126 0 16 Mar 2020
Measuring and improving the quality of visual explanations Agnieszka Grabska-Barwiñska XAI FAtt 14 3 0 14 Mar 2020
IROF: a low resource evaluation metric for explanation methods Laura Rieger Lars Kai Hansen 20 55 0 09 Mar 2020
A Survey on Deep Hashing Methods Xiao Luo Haixin Wang Huasong Zhong C. L. Philip Chen Minghua Deng Jianqiang Huang Xiansheng Hua 33 170 0 04 Mar 2020
Breaking Batch Normalization for better explainability of Deep Neural Networks through Layer-wise Relevance Propagation M. Guillemot C. Heusele R. Korichi S. Schnebert Liming Luke Chen FAtt 6 18 0 24 Feb 2020
Interpreting Interpretations: Organizing Attribution Methods by Criteria Zifan Wang Piotr (Peter) Mardziel Anupam Datta Matt Fredrikson XAI FAtt 11 17 0 19 Feb 2020
Supporting DNN Safety Analysis and Retraining through Heatmap-based Unsupervised Learning Hazem M. Fahmy F. Pastore M. Bagherzadeh Lionel C. Briand AI4CE AAML 27 30 0 03 Feb 2020
Evaluating Weakly Supervised Object Localization Methods Right Junsuk Choe Seong Joon Oh Seungho Lee Sanghyuk Chun Zeynep Akata Hyunjung Shim WSOL 289 186 0 21 Jan 2020
Restricting the Flow: Information Bottlenecks for Attribution Karl Schulz Leon Sixt Federico Tombari Tim Landgraf FAtt 6 182 0 02 Jan 2020
When Explanations Lie: Why Many Modified BP Attributions Fail Leon Sixt Maximilian Granz Tim Landgraf BDL FAtt XAI 13 132 0 20 Dec 2019
Pruning by Explaining: A Novel Criterion for Deep Neural Network Pruning Seul-Ki Yeom P. Seegerer Sebastian Lapuschkin Alexander Binder Simon Wiedemann K. Müller Wojciech Samek CVBM 13 198 0 18 Dec 2019
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 33 50 0 16 Dec 2019
Exploratory Not Explanatory: Counterfactual Analysis of Saliency Maps for Deep Reinforcement Learning Akanksha Atrey Kaleigh Clary David D. Jensen FAtt LRM 11 90 0 09 Dec 2019
Counterfactual Explanation Algorithms for Behavioral and Textual Data Yanou Ramon David Martens F. Provost Theodoros Evgeniou FAtt 17 87 0 04 Dec 2019
Explainable artificial intelligence model to predict acute critical illness from electronic health records S. Lauritsen Mads Kristensen Mathias Vassard Olsen Morten Skaarup Larsen K. M. Lauritsen Marianne Johansson Jørgensen Jeppe Lange B. Thiesson 19 297 0 03 Dec 2019
Sanity Checks for Saliency Metrics Richard J. Tomsett Daniel Harborne Supriyo Chakraborty Prudhvi K. Gurram Alun D. Preece XAI 14 167 0 29 Nov 2019
Analysis of Explainers of Black Box Deep Neural Networks for Computer Vision: A Survey Vanessa Buhrmester David Münch Michael Arens MLAU FaML XAI AAML 19 354 0 27 Nov 2019
Efficient Saliency Maps for Explainable AI T. Nathan Mundhenk Barry Y. Chen Gerald Friedland XAI FAtt 21 73 0 26 Nov 2019
Improving Feature Attribution through Input-specific Network Pruning Ashkan Khakzar Soroosh Baselizadeh Saurabh Khanduja Christian Rupprecht S. T. Kim Nassir Navab FAtt 6 11 0 25 Nov 2019
A psychophysics approach for quantitative comparison of interpretable computer vision models F. Biessmann D. Refiano 6 5 0 24 Nov 2019