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Interpretable Convolutional Neural Networks

2 October 2017

Papers citing "Interpretable Convolutional Neural Networks"

50 / 314 papers shown

Title
Training Deep Models to be Explained with Fewer Examples Tomoharu Iwata Yuya Yoshikawa FAtt 99 1 0 07 Dec 2021
STEEX: Steering Counterfactual Explanations with Semantics P. Jacob Éloi Zablocki H. Ben-younes Mickaël Chen P. Pérez Matthieu Cord 79 43 0 17 Nov 2021
Impact of loss functions on the performance of a deep neural network designed to restore low-dose digital mammography Hongming Shan R. Vimieiro L. Borges M. Vieira Ge Wang MedIm 61 11 0 12 Nov 2021
ODMTCNet: An Interpretable Multi-view Deep Neural Network Architecture for Image Feature Representation Lei Gao Zheng Guo L. Guan 69 3 0 28 Oct 2021
TSGB: Target-Selective Gradient Backprop for Probing CNN Visual Saliency Lin Cheng Pengfei Fang Yanjie Liang Liao Zhang Chunhua Shen Hanzi Wang FAtt 133 12 0 11 Oct 2021
Trustworthy AI: From Principles to Practices Yue Liu Peng Qi Bo Liu Shuai Di Jingen Liu Jiquan Pei Jinfeng Yi Bowen Zhou 213 383 0 04 Oct 2021
Learning Interpretable Concept Groups in CNNs Saurabh Varshneya Antoine Ledent Robert A. Vandermeulen Yunwen Lei Matthias Enders Damian Borth Marius Kloft 103 6 0 21 Sep 2021
CX-ToM: Counterfactual Explanations with Theory-of-Mind for Enhancing Human Trust in Image Recognition Models Arjun Reddy Akula Keze Wang Changsong Liu Sari Saba-Sadiya Hongjing Lu S. Todorovic J. Chai Song-Chun Zhu 104 49 0 03 Sep 2021
Spatio-Temporal Perturbations for Video Attribution Zhenqiang Li Weimin Wang Zuoyue Li Yifei Huang Yoichi Sato 60 6 0 01 Sep 2021
This looks more like that: Enhancing Self-Explaining Models by Prototypical Relevance Propagation Srishti Gautam Marina M.-C. Höhne Stine Hansen Robert Jenssen Michael C. Kampffmeyer 68 49 0 27 Aug 2021
Towards Interpretable Deep Metric Learning with Structural Matching Wenliang Zhao Yongming Rao Ziyi Wang Jiwen Lu Jie Zhou FedML 68 47 0 12 Aug 2021
Towards Interpretable Deep Networks for Monocular Depth Estimation Zunzhi You Yi-Hsuan Tsai W. Chiu Guanbin Li FAtt 77 17 0 11 Aug 2021
Human-in-the-loop Extraction of Interpretable Concepts in Deep Learning Models Zhenge Zhao Panpan Xu C. Scheidegger Liu Ren 48 39 0 08 Aug 2021
Mixture of Linear Models Co-supervised by Deep Neural Networks Beomseok Seo Lin Lin Jia Li 37 6 0 05 Aug 2021
Dynamic Neural Network Architectural and Topological Adaptation and Related Methods -- A Survey Lorenz Kummer AI4CE 68 0 0 28 Jul 2021
Interpretable Compositional Convolutional Neural Networks Wen Shen Zhihua Wei Shikun Huang Binbin Zhang Jiaqi Fan Ping Zhao Quanshi Zhang FAtt 70 36 0 09 Jul 2021
When and How to Fool Explainable Models (and Humans) with Adversarial Examples Jon Vadillo Roberto Santana Jose A. Lozano SILM AAML 97 13 0 05 Jul 2021
Prediction of Hereditary Cancers Using Neural Networks Zoe Guan Giovanni Parmigiani D. Braun L. Trippa MedIm 96 0 0 25 Jun 2021
Towards Fully Interpretable Deep Neural Networks: Are We There Yet? Sandareka Wickramanayake Wynne Hsu Mong Li Lee FaML AI4CE 41 3 0 24 Jun 2021
Synthetic Benchmarks for Scientific Research in Explainable Machine Learning Yang Liu Sujay Khandagale Colin White Willie Neiswanger 115 67 0 23 Jun 2021
A Game-Theoretic Taxonomy of Visual Concepts in DNNs Xu Cheng Chuntung Chu Yi Zheng Jie Ren Quanshi Zhang 47 22 0 21 Jun 2021
Thinking Like Transformers Gail Weiss Yoav Goldberg Eran Yahav AI4CE 131 135 0 13 Jun 2021
Drop Clause: Enhancing Performance, Interpretability and Robustness of the Tsetlin Machine Jivitesh Sharma Rohan Kumar Yadav Ole-Christoffer Granmo Lei Jiao VLM 58 12 0 30 May 2021
How to Explain Neural Networks: an Approximation Perspective Hangcheng Dong Bingguo Liu Fengdong Chen Dong Ye Guodong Liu FAtt 46 1 0 17 May 2021
Improving Molecular Graph Neural Network Explainability with Orthonormalization and Induced Sparsity Ryan Henderson Djork-Arné Clevert F. Montanari 91 27 0 11 May 2021
Carrying out CNN Channel Pruning in a White Box Yuxin Zhang Mingbao Lin Chia-Wen Lin Jie Chen Feiyue Huang Yongjian Wu Yonghong Tian Rongrong Ji VLM 109 61 0 24 Apr 2021
Improving Attribution Methods by Learning Submodular Functions Piyushi Manupriya Tarun Ram Menta S. Jagarlapudi V. Balasubramanian TDI 88 6 0 19 Apr 2021
An Overview of Human Activity Recognition Using Wearable Sensors: Healthcare and Artificial Intelligence Rex Liu Albara Ah Ramli Huan Zhang Esha Datta Xin Liu 55 48 0 29 Mar 2021
Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges Cynthia Rudin Chaofan Chen Zhi Chen Haiyang Huang Lesia Semenova Chudi Zhong FaML AI4CE LRM 240 677 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 82 341 0 19 Mar 2021
Danish Fungi 2020 -- Not Just Another Image Recognition Dataset Lukás Picek Milan Šulc Jirí Matas J. Heilmann‐Clausen T. Jeppesen T. Læssøe T. Frøslev 99 56 0 18 Mar 2021
Quantitative Performance Assessment of CNN Units via Topological Entropy Calculation Yang Zhao Hao Zhang 77 7 0 17 Mar 2021
Unveiling the Potential of Structure Preserving for Weakly Supervised Object Localization Xingjia Pan Yingguo Gao Zhiwen Lin Fan Tang Weiming Dong Haolei Yuan Feiyue Huang Changsheng Xu WSOL 89 87 0 08 Mar 2021
CoDeGAN: Contrastive Disentanglement for Generative Adversarial Network Lili Pan Peijun Tang Zhiyong Chen Zenglin Xu GAN DRL 64 5 0 05 Mar 2021
Human-Understandable Decision Making for Visual Recognition Xiaowei Zhou Jie Yin Ivor Tsang Chen Wang FAtt HAI 54 1 0 05 Mar 2021
Deep learning based electrical noise removal enables high spectral optoacoustic contrast in deep tissue C. Dehner Ivan Olefir K. Chowdhury D. Jüstel V. Ntziachristos 73 28 0 24 Feb 2021
VitrAI -- Applying Explainable AI in the Real World Marc Hanussek Falko Kötter Maximilien Kintz Jens Drawehn 37 2 0 12 Feb 2021
PatchX: Explaining Deep Models by Intelligible Pattern Patches for Time-series Classification Dominique Mercier Andreas Dengel Sheraz Ahmed AI4TS 44 5 0 11 Feb 2021
HYDRA: Hypergradient Data Relevance Analysis for Interpreting Deep Neural Networks Yuanyuan Chen Boyang Albert Li Han Yu Pengcheng Wu Chunyan Miao TDI 94 42 0 04 Feb 2021
A Survey on Understanding, Visualizations, and Explanation of Deep Neural Networks Atefeh Shahroudnejad FaML AAML AI4CE XAI 119 36 0 02 Feb 2021
Explaining Natural Language Processing Classifiers with Occlusion and Language Modeling David Harbecke AAML 51 2 0 28 Jan 2021
CORL: Compositional Representation Learning for Few-Shot Classification Ju He Adam Kortylewski Alan Yuille OCL 64 10 0 28 Jan 2021
Beyond Expertise and Roles: A Framework to Characterize the Stakeholders of Interpretable Machine Learning and their Needs Harini Suresh Steven R. Gomez K. Nam Arvind Satyanarayan 96 135 0 24 Jan 2021
i-Algebra: Towards Interactive Interpretability of Deep Neural Networks Xinyang Zhang Ren Pang S. Ji Fenglong Ma Ting Wang HAI AI4CE 38 5 0 22 Jan 2021
Explainability of deep vision-based autonomous driving systems: Review and challenges Éloi Zablocki H. Ben-younes P. Pérez Matthieu Cord XAI 186 177 0 13 Jan 2021
Comprehensible Convolutional Neural Networks via Guided Concept Learning Sandareka Wickramanayake Wynne Hsu Mong Li Lee SSL 52 25 0 11 Jan 2021
A Survey on Neural Network Interpretability Yu Zhang Peter Tiño A. Leonardis K. Tang FaML XAI 209 688 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 30 0 0 21 Dec 2020
MA-Unet: An improved version of Unet based on multi-scale and attention mechanism for medical image segmentation Yutong Cai Yong Wang SSeg 83 74 0 20 Dec 2020
Rule Extraction from Binary Neural Networks with Convolutional Rules for Model Validation Sophie Burkhardt Jannis Brugger Nicolas Wagner Zahra Ahmadi Kristian Kersting Stefan Kramer NAI FAtt 83 8 0 15 Dec 2020