Less is More: The Influence of Pruning on the Explainability of CNNs

17 February 2023

Martin Nocker

Papers citing "Less is More: The Influence of Pruning on the Explainability of CNNs"

50 / 51 papers shown

Title
Pruning in the Face of Adversaries F. Merkle Maximilian Samsinger Pascal Schöttle AAML CVBM 44 3 0 19 Aug 2021
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows Ze Liu Yutong Lin Yue Cao Han Hu Yixuan Wei Zheng Zhang Stephen Lin B. Guo ViT 423 21,347 0 25 Mar 2021
Socially Responsible AI Algorithms: Issues, Purposes, and Challenges Lu Cheng Kush R. Varshney Huan Liu FaML 105 150 0 01 Jan 2021
A Survey on the Explainability of Supervised Machine Learning Nadia Burkart Marco F. Huber FaML XAI 48 773 0 16 Nov 2020
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale Alexey Dosovitskiy Lucas Beyer Alexander Kolesnikov Dirk Weissenborn Xiaohua Zhai ... Matthias Minderer G. Heigold Sylvain Gelly Jakob Uszkoreit N. Houlsby ViT 557 40,961 0 22 Oct 2020
Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challenges Christoph Molnar Giuseppe Casalicchio B. Bischl AI4TS AI4CE 71 402 0 19 Oct 2020
Utilizing Explainable AI for Quantization and Pruning of Deep Neural Networks Muhammad Sabih Frank Hannig J. Teich MQ 82 24 0 20 Aug 2020
The role of explainability in creating trustworthy artificial intelligence for health care: a comprehensive survey of the terminology, design choices, and evaluation strategies A. Markus J. Kors P. Rijnbeek 80 465 0 31 Jul 2020
On quantitative aspects of model interpretability An-phi Nguyen María Rodríguez Martínez 43 114 0 15 Jul 2020
Explainable Deep Learning: A Field Guide for the Uninitiated Gabrielle Ras Ning Xie Marcel van Gerven Derek Doran AAML XAI 95 377 0 30 Apr 2020
Streamlining Tensor and Network Pruning in PyTorch Michela Paganini Jessica Zosa Forde 34 12 0 28 Apr 2020
What is the State of Neural Network Pruning? Davis W. Blalock Jose Javier Gonzalez Ortiz Jonathan Frankle John Guttag 258 1,047 0 06 Mar 2020
Pruning by Explaining: A Novel Criterion for Deep Neural Network Pruning Seul-Ki Yeom P. Seegerer Sebastian Lapuschkin Alexander Binder Simon Wiedemann K. Müller Wojciech Samek CVBM 53 207 0 18 Dec 2019
Towards Explainable Deep Neural Networks (xDNN) Plamen Angelov Eduardo Soares AAML 61 261 0 05 Dec 2019
Improving Feature Attribution through Input-specific Network Pruning Ashkan Khakzar Soroosh Baselizadeh Saurabh Khanduja Christian Rupprecht S. T. Kim Nassir Navab FAtt 36 11 0 25 Nov 2019
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI Alejandro Barredo Arrieta Natalia Díaz Rodríguez Javier Del Ser Adrien Bennetot Siham Tabik ... S. Gil-Lopez Daniel Molina Richard Benjamins Raja Chatila Francisco Herrera XAI 116 6,251 0 22 Oct 2019
A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAI Erico Tjoa Cuntai Guan XAI 89 1,446 0 17 Jul 2019
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks Mingxing Tan Quoc V. Le 3DV MedIm 131 18,106 0 28 May 2019
The State of Sparsity in Deep Neural Networks Trevor Gale Erich Elsen Sara Hooker 147 758 0 25 Feb 2019
Quantifying Interpretability and Trust in Machine Learning Systems Philipp Schmidt F. Biessmann 45 113 0 20 Jan 2019
Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead Cynthia Rudin ELM FaML 50 219 0 26 Nov 2018
Rethinking the Value of Network Pruning Zhuang Liu Mingjie Sun Tinghui Zhou Gao Huang Trevor Darrell 36 1,471 0 11 Oct 2018
Sanity Checks for Saliency Maps Julius Adebayo Justin Gilmer M. Muelly Ian Goodfellow Moritz Hardt Been Kim FAtt AAML XAI 123 1,965 0 08 Oct 2018
Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks Yang He Guoliang Kang Xuanyi Dong Yanwei Fu Yi Yang AAML VLM 60 963 0 21 Aug 2018
Techniques for Interpretable Machine Learning Mengnan Du Ninghao Liu Xia Hu FaML 77 1,090 0 31 Jul 2018
A Benchmark for Interpretability Methods in Deep Neural Networks Sara Hooker D. Erhan Pieter-Jan Kindermans Been Kim FAtt UQCV 98 681 0 28 Jun 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 83 1,858 0 31 May 2018
A Systematic DNN Weight Pruning Framework using Alternating Direction Method of Multipliers Tianyun Zhang Shaokai Ye Kaiqi Zhang Jian Tang Wujie Wen M. Fardad Yanzhi Wang 57 438 0 10 Apr 2018
Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges Gabrielle Ras Marcel van Gerven W. Haselager XAI 91 219 0 20 Mar 2018
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks Jonathan Frankle Michael Carbin 223 3,461 0 09 Mar 2018
A Survey Of Methods For Explaining Black Box Models Riccardo Guidotti A. Monreale Salvatore Ruggieri Franco Turini D. Pedreschi F. Giannotti XAI 124 3,954 0 06 Feb 2018
Interpreting Convolutional Neural Networks Through Compression R. Abbasi-Asl Bin Yu FAtt 30 21 0 07 Nov 2017
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks Aditya Chattopadhyay Anirban Sarkar Prantik Howlader V. Balasubramanian FAtt 103 2,289 0 30 Oct 2017
Data-Driven Sparse Structure Selection for Deep Neural Networks Zehao Huang Naiyan Wang 83 561 0 05 Jul 2017
Explanation in Artificial Intelligence: Insights from the Social Sciences Tim Miller XAI 239 4,259 0 22 Jun 2017
A Unified Approach to Interpreting Model Predictions Scott M. Lundberg Su-In Lee FAtt 1.1K 21,815 0 22 May 2017
Learning Important Features Through Propagating Activation Differences Avanti Shrikumar Peyton Greenside A. Kundaje FAtt 190 3,869 0 10 Apr 2017
Axiomatic Attribution for Deep Networks Mukund Sundararajan Ankur Taly Qiqi Yan OOD FAtt 175 5,986 0 04 Mar 2017
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 382 3,785 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 270 19,981 0 07 Oct 2016
European Union regulations on algorithmic decision-making and a "right to explanation" B. Goodman Seth Flaxman FaML AILaw 63 1,899 0 28 Jun 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 1.2K 16,931 0 16 Feb 2016
Learning Deep Features for Discriminative Localization Bolei Zhou A. Khosla Àgata Lapedriza A. Oliva Antonio Torralba SSL SSeg FAtt 243 9,305 0 14 Dec 2015
Deep Residual Learning for Image Recognition Kaiming He Xinming Zhang Shaoqing Ren Jian Sun MedIm 2.1K 193,814 0 10 Dec 2015
Visualizing Deep Convolutional Neural Networks Using Natural Pre-Images Aravindh Mahendran Andrea Vedaldi FAtt 68 534 0 07 Dec 2015
Evaluating the visualization of what a Deep Neural Network has learned Wojciech Samek Alexander Binder G. Montavon Sebastian Lapuschkin K. Müller XAI 132 1,192 0 21 Sep 2015
Learning both Weights and Connections for Efficient Neural Networks Song Han Jeff Pool J. Tran W. Dally CVBM 306 6,669 0 08 Jun 2015
Going Deeper with Convolutions Christian Szegedy Wei Liu Yangqing Jia P. Sermanet Scott E. Reed Dragomir Anguelov D. Erhan Vincent Vanhoucke Andrew Rabinovich 424 43,635 0 17 Sep 2014
Very Deep Convolutional Networks for Large-Scale Image Recognition Karen Simonyan Andrew Zisserman FAtt MDE 1.6K 100,330 0 04 Sep 2014
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps Karen Simonyan Andrea Vedaldi Andrew Zisserman FAtt 303 7,289 0 20 Dec 2013