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
Interpretable Neural Networks with Frank-Wolfe: Sparse Relevance Maps and Relevance Orderings Jan Macdonald Mathieu Besançon Sebastian Pokutta 32 11 0 15 Oct 2021
Fine-Grained Neural Network Explanation by Identifying Input Features with Predictive Information Yang Zhang Ashkan Khakzar Yawei Li Azade Farshad Seong Tae Kim Nassir Navab FAtt XAI 57 27 0 04 Oct 2021
Discriminative Attribution from Counterfactuals N. Eckstein A. S. Bates G. Jefferis Jan Funke FAtt CML 27 1 0 28 Sep 2021
Awakening Latent Grounding from Pretrained Language Models for Semantic Parsing Qian Liu Dejian Yang Jiahui Zhang Jiaqi Guo Bin Zhou Jian-Guang Lou 51 41 0 22 Sep 2021
FUTURE-AI: Guiding Principles and Consensus Recommendations for Trustworthy Artificial Intelligence in Medical Imaging Karim Lekadira Richard Osuala C. Gallin Noussair Lazrak Kaisar Kushibar ... Nickolas Papanikolaou Zohaib Salahuddin Henry C. Woodruff Philippe Lambin L. Martí-Bonmatí AI4TS 71 56 0 20 Sep 2021
Detection Accuracy for Evaluating Compositional Explanations of Units Sayo M. Makinwa Biagio La Rosa Roberto Capobianco FAtt CoGe 41 1 0 16 Sep 2021
Logic Traps in Evaluating Attribution Scores Yiming Ju Yuanzhe Zhang Zhao Yang Zhongtao Jiang Kang Liu Jun Zhao XAI FAtt 25 18 0 12 Sep 2021
Cross-Model Consensus of Explanations and Beyond for Image Classification Models: An Empirical Study Xuhong Li Haoyi Xiong Siyu Huang Shilei Ji Dejing Dou 22 10 0 02 Sep 2021
Towards Improving Adversarial Training of NLP Models Jin Yong Yoo Yanjun Qi AAML 6 123 0 01 Sep 2021
Towards Learning a Vocabulary of Visual Concepts and Operators using Deep Neural Networks Sunil Kumar Vengalil N. Sinha 11 0 0 01 Sep 2021
Spatio-Temporal Perturbations for Video Attribution Zhenqiang Li Weimin Wang Zuoyue Li Yifei Huang Yoichi Sato 16 6 0 01 Sep 2021
Calibrating Class Activation Maps for Long-Tailed Visual Recognition Chi Zhang Guosheng Lin Lvlong Lai Henghui Ding Qingyao Wu 29 1 0 29 Aug 2021
Explaining Bayesian Neural Networks Kirill Bykov Marina M.-C. Höhne Adelaida Creosteanu Klaus-Robert Muller Frederick Klauschen Shinichi Nakajima Marius Kloft BDL AAML 34 25 0 23 Aug 2021
Challenges for cognitive decoding using deep learning methods A. Thomas Christopher Ré R. Poldrack AI4CE 24 6 0 16 Aug 2021
Where do Models go Wrong? Parameter-Space Saliency Maps for Explainability Roman Levin Manli Shu Eitan Borgnia Furong Huang Micah Goldblum Tom Goldstein FAtt AAML 25 10 0 03 Aug 2021
Surrogate Model-Based Explainability Methods for Point Cloud NNs Hanxiao Tan Helena Kotthaus 3DPC 23 26 0 28 Jul 2021
Normalization Matters in Weakly Supervised Object Localization Jeesoo Kim Junsuk Choe Sangdoo Yun Nojun Kwak WSOL 34 41 0 28 Jul 2021
Robust Explainability: A Tutorial on Gradient-Based Attribution Methods for Deep Neural Networks Ian E. Nielsen Dimah Dera Ghulam Rasool N. Bouaynaya R. Ramachandran FAtt 16 79 0 23 Jul 2021
Quality Metrics for Transparent Machine Learning With and Without Humans In the Loop Are Not Correlated F. Biessmann D. Refiano 10 10 0 01 Jul 2021
Towards Measuring Bias in Image Classification Nina Schaaf Omar de Mitri Hang Beom Kim A. Windberger Marco F. Huber SSL 34 10 0 01 Jul 2021
Crowdsourcing Evaluation of Saliency-based XAI Methods Xiaotian Lu A. Tolmachev Tatsuya Yamamoto Koh Takeuchi Seiji Okajima T. Takebayashi Koji Maruhashi H. Kashima XAI FAtt 8 14 0 27 Jun 2021
On the Robustness of Pretraining and Self-Supervision for a Deep Learning-based Analysis of Diabetic Retinopathy Vignesh Srinivasan Nils Strodthoff Jackie Ma Alexander Binder Klaus-Robert Muller Wojciech Samek OOD 20 6 0 25 Jun 2021
Software for Dataset-wide XAI: From Local Explanations to Global Insights with Zennit, CoRelAy, and ViRelAy Christopher J. Anders David Neumann Wojciech Samek K. Müller Sebastian Lapuschkin 29 64 0 24 Jun 2021
Synthetic Benchmarks for Scientific Research in Explainable Machine Learning Yang Liu Sujay Khandagale Colin White W. Neiswanger 37 65 0 23 Jun 2021
Reachability Analysis of Convolutional Neural Networks Xiaodong Yang Tomoya Yamaguchi Hoang-Dung Tran Bardh Hoxha Taylor T. Johnson Danil Prokhorov FAtt 13 5 0 22 Jun 2021
NoiseGrad: Enhancing Explanations by Introducing Stochasticity to Model Weights Kirill Bykov Anna Hedström Shinichi Nakajima Marina M.-C. Höhne FAtt 17 34 0 18 Jun 2021
Developing a Fidelity Evaluation Approach for Interpretable Machine Learning M. Velmurugan Chun Ouyang Catarina Moreira Renuka Sindhgatta XAI 27 16 0 16 Jun 2021
Keep CALM and Improve Visual Feature Attribution Jae Myung Kim Junsuk Choe Zeynep Akata Seong Joon Oh FAtt 342 20 0 15 Jun 2021
Exploiting auto-encoders and segmentation methods for middle-level explanations of image classification systems Andrea Apicella Salvatore Giugliano Francesco Isgrò R. Prevete 11 18 0 09 Jun 2021
Taxonomy of Machine Learning Safety: A Survey and Primer Sina Mohseni Haotao Wang Zhiding Yu Chaowei Xiao Zhangyang Wang J. Yadawa 21 31 0 09 Jun 2021
On the Lack of Robust Interpretability of Neural Text Classifiers Muhammad Bilal Zafar Michele Donini Dylan Slack Cédric Archambeau Sanjiv Ranjan Das K. Kenthapadi AAML 11 21 0 08 Jun 2021
Topological Measurement of Deep Neural Networks Using Persistent Homology Satoru Watanabe Hayato Yamana 17 16 0 06 Jun 2021
Evaluating Local Explanations using White-box Models Amir Hossein Akhavan Rahnama Judith Butepage Pierre Geurts Henrik Bostrom FAtt 27 0 0 04 Jun 2021
To trust or not to trust an explanation: using LEAF to evaluate local linear XAI methods E. Amparore Alan Perotti P. Bajardi FAtt 17 68 0 01 Jun 2021
Balancing Robustness and Sensitivity using Feature Contrastive Learning Seungyeon Kim Daniel Glasner Srikumar Ramalingam Cho-Jui Hsieh Kishore Papineni Sanjiv Kumar 25 1 0 19 May 2021
Do Feature Attribution Methods Correctly Attribute Features? Yilun Zhou Serena Booth Marco Tulio Ribeiro J. Shah FAtt XAI 33 132 0 27 Apr 2021
Improving Attribution Methods by Learning Submodular Functions Piyushi Manupriya Tarun Ram Menta S. Jagarlapudi V. Balasubramanian TDI 22 6 0 19 Apr 2021
Flexible Instance-Specific Rationalization of NLP Models G. Chrysostomou Nikolaos Aletras 31 14 0 16 Apr 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 26 6 0 14 Apr 2021
Enhancing Deep Neural Network Saliency Visualizations with Gradual Extrapolation Tomasz Szandała FAtt 14 4 0 11 Apr 2021
Explaining Neural Network Predictions on Sentence Pairs via Learning Word-Group Masks Hanjie Chen Song Feng Jatin Ganhotra H. Wan Chulaka Gunasekara Sachindra Joshi Yangfeng Ji 13 17 0 09 Apr 2021
Robust Semantic Interpretability: Revisiting Concept Activation Vectors J. Pfau A. Young Jerome Wei Maria L. Wei Michael J. Keiser FAtt 33 14 0 06 Apr 2021
White Box Methods for Explanations of Convolutional Neural Networks in Image Classification Tasks Meghna P. Ayyar J. Benois-Pineau A. Zemmari FAtt 14 17 0 06 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
Explaining COVID-19 and Thoracic Pathology Model Predictions by Identifying Informative Input Features Ashkan Khakzar Yang Zhang W. Mansour Yuezhi Cai Yawei Li Yucheng Zhang Seong Tae Kim Nassir Navab FAtt 49 17 0 01 Apr 2021
Neural Response Interpretation through the Lens of Critical Pathways Ashkan Khakzar Soroosh Baselizadeh Saurabh Khanduja Christian Rupprecht Seong Tae Kim Nassir Navab 29 32 0 31 Mar 2021
Group-CAM: Group Score-Weighted Visual Explanations for Deep Convolutional Networks Qing-Long Zhang Lu Rao Yubin Yang 16 58 0 25 Mar 2021
ECINN: Efficient Counterfactuals from Invertible Neural Networks Frederik Hvilshoj Alexandros Iosifidis Ira Assent BDL 24 26 0 25 Mar 2021
Robust Models Are More Interpretable Because Attributions Look Normal Zifan Wang Matt Fredrikson Anupam Datta OOD FAtt 35 25 0 20 Mar 2021
Interpretable Deep Learning: Interpretation, Interpretability, Trustworthiness, and Beyond Xuhong Li Haoyi Xiong Xingjian Li Xuanyu Wu Xiao Zhang Ji Liu Jiang Bian Dejing Dou AAML FaML XAI HAI 23 317 0 19 Mar 2021