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Summit: Scaling Deep Learning Interpretability by Visualizing Activation and Attribution Summarizations

4 April 2019

Papers citing "Summit: Scaling Deep Learning Interpretability by Visualizing Activation and Attribution Summarizations"

32 / 32 papers shown

Title
Interactivity x Explainability: Toward Understanding How Interactivity Can Improve Computer Vision Explanations Indu Panigrahi Sunnie S. Y. Kim Amna Liaqat Rohan Jinturkar Olga Russakovsky Ruth C. Fong Parastoo Abtahi FAtt HAI 237 1 0 14 Apr 2025
The Role of Domain Expertise in User Trust and the Impact of First Impressions with Intelligent Systems Mahsan Nourani J. King Eric D. Ragan 73 100 0 20 Aug 2020
Analyzing the Noise Robustness of Deep Neural Networks Kelei Cao Mengchen Liu Hang Su Jing Wu Jun Zhu Shixia Liu AAML 127 90 0 26 Jan 2020
GAN Lab: Understanding Complex Deep Generative Models using Interactive Visual Experimentation Minsuk Kahng Nikhil Thorat Duen Horng Chau F. Viégas Martin Wattenberg HAI GAN 55 173 0 05 Sep 2018
Choose Your Neuron: Incorporating Domain Knowledge through Neuron-Importance Ramprasaath R. Selvaraju Prithvijit Chattopadhyay Mohamed Elhoseiny Tilak Sharma Dhruv Batra Devi Parikh Stefan Lee 81 37 0 08 Aug 2018
Explaining Explanations: An Overview of Interpretability of Machine Learning Leilani H. Gilpin David Bau Ben Z. Yuan Ayesha Bajwa Michael A. Specter Lalana Kagal XAI 112 1,863 0 31 May 2018
Taskonomy: Disentangling Task Transfer Learning Amir Zamir Alexander Sax Bokui (William) Shen Leonidas Guibas Jitendra Malik Silvio Savarese 126 1,222 0 23 Apr 2018
Shield: Fast, Practical Defense and Vaccination for Deep Learning using JPEG Compression Nilaksh Das Madhuri Shanbhogue Shang-Tse Chen Fred Hohman Siwei Li Li-Wei Chen Michael E. Kounavis Duen Horng Chau FedML AAML 85 227 0 19 Feb 2018
UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction Leland McInnes John Healy James Melville 202 9,479 0 09 Feb 2018
Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontiers Fred Hohman Minsuk Kahng Robert S. Pienta Duen Horng Chau OOD HAI 91 541 0 21 Jan 2018
Net2Vec: Quantifying and Explaining how Concepts are Encoded by Filters in Deep Neural Networks Ruth C. Fong Andrea Vedaldi FAtt 80 264 0 10 Jan 2018
Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV) Been Kim Martin Wattenberg Justin Gilmer Carrie J. Cai James Wexler F. Viégas Rory Sayres FAtt 242 1,849 0 30 Nov 2017
Do Convolutional Neural Networks Learn Class Hierarchy? B. Alsallakh Amin Jourabloo Mao Ye Xiaoming Liu Liu Ren 186 214 0 17 Oct 2017
Direct-Manipulation Visualization of Deep Networks D. Smilkov Shan Carter D. Sculley F. Viégas Martin Wattenberg FAtt AI4CE 58 140 0 12 Aug 2017
Methods for Interpreting and Understanding Deep Neural Networks G. Montavon Wojciech Samek K. Müller FaML 293 2,271 0 24 Jun 2017
SmoothGrad: removing noise by adding noise D. Smilkov Nikhil Thorat Been Kim F. Viégas Martin Wattenberg FAtt ODL 210 2,236 0 12 Jun 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 79 341 0 16 May 2017
Network Dissection: Quantifying Interpretability of Deep Visual Representations David Bau Bolei Zhou A. Khosla A. Oliva Antonio Torralba MILM FAtt 158 1,526 1 19 Apr 2017
Interpretable Explanations of Black Boxes by Meaningful Perturbation Ruth C. Fong Andrea Vedaldi FAtt AAML 83 1,525 0 11 Apr 2017
ActiVis: Visual Exploration of Industry-Scale Deep Neural Network Models Minsuk Kahng Pierre Yves Andrews Aditya Kalro Duen Horng Chau HAI 75 324 0 06 Apr 2017
Axiomatic Attribution for Deep Networks Mukund Sundararajan Ankur Taly Qiqi Yan OOD FAtt 193 6,024 0 04 Mar 2017
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 410 3,820 0 28 Feb 2017
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 335 20,110 0 07 Oct 2016
The Mythos of Model Interpretability Zachary Chase Lipton FaML 183 3,708 0 10 Jun 2016
Towards Better Analysis of Deep Convolutional Neural Networks Mengchen Liu Jiaxin Shi Zerui Li Chongxuan Li Jun Zhu Shixia Liu HAI 117 477 0 24 Apr 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 1.2K 17,071 0 16 Feb 2016
Deep Residual Learning for Image Recognition Kaiming He Xinming Zhang Shaoqing Ren Jian Sun MedIm 2.2K 194,510 0 10 Dec 2015
Going Deeper with Convolutions Christian Szegedy Wei Liu Yangqing Jia P. Sermanet Scott E. Reed Dragomir Anguelov D. Erhan Vincent Vanhoucke Andrew Rabinovich 494 43,698 0 17 Sep 2014
Very Deep Convolutional Networks for Large-Scale Image Recognition Karen Simonyan Andrew Zisserman FAtt MDE 1.7K 100,529 0 04 Sep 2014
ImageNet Large Scale Visual Recognition Challenge Olga Russakovsky Jia Deng Hao Su J. Krause S. Satheesh ... A. Karpathy A. Khosla Michael S. Bernstein Alexander C. Berg Li Fei-Fei VLM ObjD 1.7K 39,615 0 01 Sep 2014
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps Karen Simonyan Andrea Vedaldi Andrew Zisserman FAtt 314 7,321 0 20 Dec 2013
Visualizing and Understanding Convolutional Networks Matthew D. Zeiler Rob Fergus FAtt SSL 603 15,907 0 12 Nov 2013