Visualizing Deep Neural Network Decisions: Prediction Difference Analysis

15 February 2017

Papers citing "Visualizing Deep Neural Network Decisions: Prediction Difference Analysis"

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Title
Explainability Is in the Mind of the Beholder: Establishing the Foundations of Explainable Artificial Intelligence Kacper Sokol Peter A. Flach 26 20 0 29 Dec 2021
Model Doctor: A Simple Gradient Aggregation Strategy for Diagnosing and Treating CNN Classifiers Zunlei Feng Jiacong Hu Sai Wu Xiaotian Yu Jie Song Mingli Song 21 13 0 09 Dec 2021
Explainable Deep Learning in Healthcare: A Methodological Survey from an Attribution View Di Jin Elena Sergeeva W. Weng Geeticka Chauhan Peter Szolovits OOD 31 54 0 05 Dec 2021
Extracting knowledge from features with multilevel abstraction Jin-Siang Lin Zhaoyang Li 8 0 0 04 Dec 2021
LIMEcraft: Handcrafted superpixel selection and inspection for Visual eXplanations Weronika Hryniewska Adrianna Grudzieñ P. Biecek FAtt 48 3 0 15 Nov 2021
Fast Axiomatic Attribution for Neural Networks Robin Hesse Simone Schaub-Meyer Stefan Roth 11 36 0 15 Nov 2021
Counterfactual Explanations for Models of Code Jürgen Cito Işıl Dillig V. Murali S. Chandra AAML LRM 24 47 0 10 Nov 2021
Visualizing the Emergence of Intermediate Visual Patterns in DNNs Mingjie Li Shaobo Wang Quanshi Zhang 16 11 0 05 Nov 2021
Automatic Sleep Staging of EEG Signals: Recent Development, Challenges, and Future Directions Huy P Phan Kaare B. Mikkelsen 11 93 0 03 Nov 2021
Discriminative Attribution from Counterfactuals N. Eckstein A. S. Bates G. Jefferis Jan Funke FAtt CML 11 1 0 28 Sep 2021
Attention Weights in Transformer NMT Fail Aligning Words Between Sequences but Largely Explain Model Predictions Javier Ferrando Marta R. Costa-jussá 14 13 0 13 Sep 2021
Instance-wise or Class-wise? A Tale of Neighbor Shapley for Concept-based Explanation Jiahui Li Kun Kuang Lin Li Long Chen Songyang Zhang Jian Shao Jun Xiao FAtt 12 17 0 03 Sep 2021
Neuron-level Interpretation of Deep NLP Models: A Survey Hassan Sajjad Nadir Durrani Fahim Dalvi MILM AI4CE 22 79 0 30 Aug 2021
Calibrating Class Activation Maps for Long-Tailed Visual Recognition Chi Zhang Guosheng Lin Lvlong Lai Henghui Ding Qingyao Wu 21 1 0 29 Aug 2021
Understanding of Kernels in CNN Models by Suppressing Irrelevant Visual Features in Images Jiafan Zhuang Wanying Tao Jianfei Xing Wei Shi Ruixuan Wang Weishi Zheng FAtt 32 3 0 25 Aug 2021
Challenges for cognitive decoding using deep learning methods A. Thomas Christopher Ré R. Poldrack AI4CE 6 6 0 16 Aug 2021
Interpreting and improving deep-learning models with reality checks Chandan Singh Wooseok Ha Bin Yu FAtt 19 3 0 16 Aug 2021
Understanding Character Recognition using Visual Explanations Derived from the Human Visual System and Deep Networks Chetan Ralekar Shubham Choudhary Tapan K. Gandhi S. Chaudhury FAtt 14 1 0 10 Aug 2021
Explainable artificial intelligence (XAI) in deep learning-based medical image analysis Bas H. M. van der Velden Hugo J. Kuijf K. Gilhuijs M. Viergever XAI 16 636 0 22 Jul 2021
Levels of explainable artificial intelligence for human-aligned conversational explanations Richard Dazeley Peter Vamplew Cameron Foale Charlotte Young Sunil Aryal F. Cruz 30 89 0 07 Jul 2021
A Review of Explainable Artificial Intelligence in Manufacturing G. Sofianidis Jože M. Rožanec Dunja Mladenić D. Kyriazis 17 17 0 05 Jul 2021
Reachability Analysis of Convolutional Neural Networks Xiaodong Yang Tomoya Yamaguchi Hoang-Dung Tran Bardh Hoxha Taylor T. Johnson Danil Prokhorov FAtt 8 5 0 22 Jun 2021
CAMERAS: Enhanced Resolution And Sanity preserving Class Activation Mapping for image saliency M. Jalwana Naveed Akhtar Bennamoun Ajmal Saeed Mian 14 54 0 20 Jun 2021
SEEN: Sharpening Explanations for Graph Neural Networks using Explanations from Neighborhoods Hyeoncheol Cho Youngrock Oh Eunjoo Jeon FAtt 19 0 0 16 Jun 2021
Keep CALM and Improve Visual Feature Attribution Jae Myung Kim Junsuk Choe Zeynep Akata Seong Joon Oh FAtt 340 20 0 15 Jun 2021
The Out-of-Distribution Problem in Explainability and Search Methods for Feature Importance Explanations Peter Hase Harry Xie Mohit Bansal OODD LRM FAtt 18 91 0 01 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 26 2 0 28 May 2021
Balancing Robustness and Sensitivity using Feature Contrastive Learning Seungyeon Kim Daniel Glasner Srikumar Ramalingam Cho-Jui Hsieh Kishore Papineni Sanjiv Kumar 17 1 0 19 May 2021
Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety Sebastian Houben Stephanie Abrecht Maram Akila Andreas Bär Felix Brockherde ... Serin Varghese Michael Weber Sebastian J. Wirkert Tim Wirtz Matthias Woehrle AAML 13 58 0 29 Apr 2021
Patch Shortcuts: Interpretable Proxy Models Efficiently Find Black-Box Vulnerabilities Julia Rosenzweig Joachim Sicking Sebastian Houben Michael Mock Maram Akila AAML 16 3 0 22 Apr 2021
A-FMI: Learning Attributions from Deep Networks via Feature Map Importance An Zhang Xiang Wang Chengfang Fang Jie Shi Tat-Seng Chua Zehua Chen FAtt 16 3 0 12 Apr 2021
Explainability-aided Domain Generalization for Image Classification Robin M. Schmidt FAtt OOD 19 1 0 05 Apr 2021
Leveraging Neural Machine Translation for Word Alignment Vilém Zouhar Daria Pylypenko 15 2 0 31 Mar 2021
Neural Response Interpretation through the Lens of Critical Pathways Ashkan Khakzar Soroosh Baselizadeh Saurabh Khanduja Christian Rupprecht Seong Tae Kim Nassir Navab 27 32 0 31 Mar 2021
Visual Explanations from Spiking Neural Networks using Interspike Intervals Youngeun Kim Priyadarshini Panda AAML FAtt 19 46 0 26 Mar 2021
ECINN: Efficient Counterfactuals from Invertible Neural Networks Frederik Hvilshoj Alexandros Iosifidis Ira Assent BDL 16 26 0 25 Mar 2021
Refining Language Models with Compositional Explanations Huihan Yao Ying Chen Qinyuan Ye Xisen Jin Xiang Ren 15 35 0 18 Mar 2021
Have We Learned to Explain?: How Interpretability Methods Can Learn to Encode Predictions in their Interpretations N. Jethani Mukund Sudarshan Yindalon Aphinyanagphongs Rajesh Ranganath FAtt 78 70 0 02 Mar 2021
Contrastive Explanations for Model Interpretability Alon Jacovi Swabha Swayamdipta Shauli Ravfogel Yanai Elazar Yejin Choi Yoav Goldberg 33 95 0 02 Mar 2021
PredDiff: Explanations and Interactions from Conditional Expectations Stefan Blücher Johanna Vielhaben Nils Strodthoff FAtt 17 19 0 26 Feb 2021
WheaCha: A Method for Explaining the Predictions of Models of Code Yu Wang Ke Wang Linzhang Wang FAtt 11 3 0 09 Feb 2021
A Survey on Understanding, Visualizations, and Explanation of Deep Neural Networks Atefeh Shahroudnejad FaML AAML AI4CE XAI 46 34 0 02 Feb 2021
Explaining Natural Language Processing Classifiers with Occlusion and Language Modeling David Harbecke AAML 19 2 0 28 Jan 2021
Generating Attribution Maps with Disentangled Masked Backpropagation Adria Ruiz Antonio Agudo Francesc Moreno FAtt 14 1 0 17 Jan 2021
A Survey on Neural Network Interpretability Yu Zhang Peter Tiño A. Leonardis K. Tang FaML XAI 139 656 0 28 Dec 2020
Image Translation via Fine-grained Knowledge Transfer Xuanhong Chen Ziang Liu Ting Qiu Bingbing Ni Naiyuan Liu Xiwei Hu Yuhan Li 14 0 0 21 Dec 2020
Explaining Black-box Models for Biomedical Text Classification M. Moradi Matthias Samwald 28 21 0 20 Dec 2020
Towards Robust Explanations for Deep Neural Networks Ann-Kathrin Dombrowski Christopher J. Anders K. Müller Pan Kessel FAtt 13 62 0 18 Dec 2020
AdjointBackMap: Reconstructing Effective Decision Hypersurfaces from CNN Layers Using Adjoint Operators Qing Wan Yoonsuck Choe 20 1 0 16 Dec 2020
Quantifying Explainers of Graph Neural Networks in Computational Pathology Guillaume Jaume Pushpak Pati Behzad Bozorgtabar Antonio Foncubierta-Rodríguez Florinda Feroce A. Anniciello T. Rau Jean-Philippe Thiran M. Gabrani O. Goksel FAtt 11 76 0 25 Nov 2020