Visual Interpretability for Deep Learning: a Survey

2 February 2018

Papers citing "Visual Interpretability for Deep Learning: a Survey"

40 / 40 papers shown

Title
Discovering Chunks in Neural Embeddings for Interpretability Shuchen Wu Stephan Alaniz Eric Schulz Zeynep Akata 82 0 0 03 Feb 2025
Generalized Task-Driven Medical Image Quality Enhancement with Gradient Promotion Dong Zhang Kwang-Ting Cheng MedIm 134 0 0 03 Jan 2025
Explaining Quantum Circuits with Shapley Values: Towards Explainable Quantum Machine Learning R. Heese Thore Gerlach Sascha Mucke Sabine Muller Matthias Jakobs Nico Piatkowski 51 19 0 22 Jan 2023
Explainability in Deep Reinforcement Learning Alexandre Heuillet Fabien Couthouis Natalia Díaz Rodríguez XAI 160 282 0 15 Aug 2020
Interpreting CNNs via Decision Trees Quanshi Zhang Yu Yang Ying Nian Wu Song-Chun Zhu FAtt 70 323 0 01 Feb 2018
Towards Interpretable R-CNN by Unfolding Latent Structures Tianfu Wu Wei Sun Xilai Li Xi Song Yangqiu Song ObjD 30 20 0 14 Nov 2017
Examining CNN Representations with respect to Dataset Bias Quanshi Zhang Wenguan Wang Song-Chun Zhu SSL FAtt 51 104 0 29 Oct 2017
Dynamic Routing Between Capsules S. Sabour Nicholas Frosst Geoffrey E. Hinton 174 4,596 0 26 Oct 2017
One pixel attack for fooling deep neural networks Jiawei Su Danilo Vasconcellos Vargas Kouichi Sakurai AAML 127 2,324 0 24 Oct 2017
Interpretable Convolutional Neural Networks Quanshi Zhang Ying Nian Wu Song-Chun Zhu FAtt 70 781 0 02 Oct 2017
Interpreting CNN Knowledge via an Explanatory Graph Quanshi Zhang Ruiming Cao Feng Shi Ying Nian Wu Song-Chun Zhu FAtt GNN SSL 56 242 0 05 Aug 2017
Interactively Transferring CNN Patterns for Part Localization Quanshi Zhang Ruiming Cao Shengming Zhang Mark Edmonds Ying Nian Wu Song-Chun Zhu 30 16 0 05 Aug 2017
Learning how to explain neural networks: PatternNet and PatternAttribution Pieter-Jan Kindermans Kristof T. Schütt Maximilian Alber K. Müller D. Erhan Been Kim Sven Dähne XAI FAtt 76 339 0 16 May 2017
Network Dissection: Quantifying Interpretability of Deep Visual Representations David Bau Bolei Zhou A. Khosla A. Oliva Antonio Torralba MILM FAtt 146 1,515 1 19 Apr 2017
Explaining the Unexplained: A CLass-Enhanced Attentive Response (CLEAR) Approach to Understanding Deep Neural Networks Devinder Kumar Alexander Wong Graham W. Taylor 60 61 0 13 Apr 2017
Interpretable Explanations of Black Boxes by Meaningful Perturbation Ruth C. Fong Andrea Vedaldi FAtt AAML 74 1,520 0 11 Apr 2017
Mining Object Parts from CNNs via Active Question-Answering Quanshi Zhang Ruiming Cao Ying Nian Wu Song-Chun Zhu 50 25 0 11 Apr 2017
Understanding Black-box Predictions via Influence Functions Pang Wei Koh Percy Liang TDI 210 2,894 0 14 Mar 2017
Visualizing Deep Neural Network Decisions: Prediction Difference Analysis L. Zintgraf Taco S. Cohen T. Adel Max Welling FAtt 140 708 0 15 Feb 2017
The VQA-Machine: Learning How to Use Existing Vision Algorithms to Answer New Questions Peng Wang Qi Wu Chunhua Shen Anton Van Den Hengel OOD 64 86 0 16 Dec 2016
Growing Interpretable Part Graphs on ConvNets via Multi-Shot Learning Quanshi Zhang Ruiming Cao Ying Nian Wu Song-Chun Zhu 50 70 0 14 Nov 2016
Identifying Unknown Unknowns in the Open World: Representations and Policies for Guided Exploration Himabindu Lakkaraju Ece Kamar R. Caruana Eric Horvitz 54 152 0 28 Oct 2016
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization Ramprasaath R. Selvaraju Michael Cogswell Abhishek Das Ramakrishna Vedantam Devi Parikh Dhruv Batra FAtt 303 20,023 0 07 Oct 2016
Towards Transparent AI Systems: Interpreting Visual Question Answering Models Yash Goyal Akrit Mohapatra Devi Parikh Dhruv Batra 42 74 0 31 Aug 2016
Densely Connected Convolutional Networks Gao Huang Zhuang Liu Laurens van der Maaten Kilian Q. Weinberger PINN 3DV 772 36,813 0 25 Aug 2016
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets Xi Chen Yan Duan Rein Houthooft John Schulman Ilya Sutskever Pieter Abbeel GAN 159 4,235 0 12 Jun 2016
Harnessing Deep Neural Networks with Logic Rules Zhiting Hu Xuezhe Ma Zhengzhong Liu Eduard H. Hovy Eric Xing AI4CE NAI 56 614 0 21 Mar 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 1.2K 16,990 0 16 Feb 2016
Deep Residual Learning for Image Recognition Kaiming He Xinming Zhang Shaoqing Ren Jian Sun MedIm 2.2K 194,020 0 10 Dec 2015
Inverting Visual Representations with Convolutional Networks Alexey Dosovitskiy Thomas Brox SSL FAtt 65 665 0 09 Jun 2015
Understanding deep features with computer-generated imagery Mathieu Aubry Bryan C. Russell 72 149 0 03 Jun 2015
Unsupervised Learning on Neural Network Outputs: with Application in Zero-shot Learning Yao Lu SSL 56 39 0 02 Jun 2015
Object Detectors Emerge in Deep Scene CNNs Bolei Zhou A. Khosla Àgata Lapedriza A. Oliva Antonio Torralba ObjD 145 1,283 0 22 Dec 2014
Striving for Simplicity: The All Convolutional Net Jost Tobias Springenberg Alexey Dosovitskiy Thomas Brox Martin Riedmiller FAtt 248 4,672 0 21 Dec 2014
Deep Learning Face Attributes in the Wild Ziwei Liu Ping Luo Xiaogang Wang Xiaoou Tang CVBM 244 8,408 0 28 Nov 2014
Understanding Deep Image Representations by Inverting Them Aravindh Mahendran Andrea Vedaldi FAtt 126 1,962 0 26 Nov 2014
How transferable are features in deep neural networks? J. Yosinski Jeff Clune Yoshua Bengio Hod Lipson OOD 231 8,336 0 06 Nov 2014
Intriguing properties of neural networks Christian Szegedy Wojciech Zaremba Ilya Sutskever Joan Bruna D. Erhan Ian Goodfellow Rob Fergus AAML 270 14,927 1 21 Dec 2013
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps Karen Simonyan Andrea Vedaldi Andrew Zisserman FAtt 312 7,295 0 20 Dec 2013
Visualizing and Understanding Convolutional Networks Matthew D. Zeiler Rob Fergus FAtt SSL 595 15,882 0 12 Nov 2013