Interpretable Explanations of Black Boxes by Meaningful Perturbation

11 April 2017

Andrea Vedaldi

Papers citing "Interpretable Explanations of Black Boxes by Meaningful Perturbation"

29 / 29 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 125 1 0 14 Apr 2025
Class-Dependent Perturbation Effects in Evaluating Time Series Attributions Gregor Baer Isel Grau Chao Zhang Pieter Van Gorp AAML 57 0 0 24 Feb 2025
Generating visual explanations from deep networks using implicit neural representations Michal Byra Henrik Skibbe GAN FAtt 51 0 0 20 Jan 2025
Faithful Counterfactual Visual Explanations (FCVE) Bismillah Khan Syed Ali Tariq Tehseen Zia Muhammad Ahsan David Windridge 46 0 0 12 Jan 2025
Attribution Analysis Meets Model Editing: Advancing Knowledge Correction in Vision Language Models with VisEdit Qizhou Chen Taolin Zhang Chengyu Wang Xiaofeng He Dakan Wang Tingting Liu KELM 84 3 0 19 Aug 2024
Interpreting Low-level Vision Models with Causal Effect Maps Jinfan Hu Jinjin Gu Shiyao Yu Fanghua Yu Zheyuan Li Zhiyuan You Chaochao Lu Chao Dong CML 100 2 0 29 Jul 2024
MambaLRP: Explaining Selective State Space Sequence Models F. Jafari G. Montavon Klaus-Robert Müller Oliver Eberle Mamba 136 9 0 11 Jun 2024
CONFINE: Conformal Prediction for Interpretable Neural Networks Linhui Huang S. Lala N. Jha 149 2 0 01 Jun 2024
Listenable Maps for Zero-Shot Audio Classifiers Francesco Paissan Luca Della Libera Mirco Ravanelli Cem Subakan 47 4 0 27 May 2024
Safety Implications of Explainable Artificial Intelligence in End-to-End Autonomous Driving Shahin Atakishiyev Mohammad Salameh Randy Goebel 124 6 0 18 Mar 2024
SurvLIME: A method for explaining machine learning survival models M. Kovalev Lev V. Utkin E. Kasimov 161 90 0 18 Mar 2020
Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications Wojciech Samek G. Montavon Sebastian Lapuschkin Christopher J. Anders K. Müller XAI 78 82 0 17 Mar 2020
SuperMix: Supervising the Mixing Data Augmentation Ali Dabouei Sobhan Soleymani Fariborz Taherkhani Nasser M. Nasrabadi 42 98 0 10 Mar 2020
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 185 19,796 0 07 Oct 2016
Top-down Neural Attention by Excitation Backprop Jianming Zhang Zhe Lin Jonathan Brandt Xiaohui Shen Stan Sclaroff 44 945 0 01 Aug 2016
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 480 5,868 0 08 Jul 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 422 16,765 0 16 Feb 2016
Learning Deep Features for Discriminative Localization Bolei Zhou A. Khosla Àgata Lapedriza A. Oliva Antonio Torralba SSL SSeg FAtt 123 9,266 0 14 Dec 2015
Visualizing Deep Convolutional Neural Networks Using Natural Pre-Images Aravindh Mahendran Andrea Vedaldi FAtt 37 532 0 07 Dec 2015
Adam: A Method for Stochastic Optimization Diederik P. Kingma Jimmy Ba ODL 624 149,474 0 22 Dec 2014
Object Detectors Emerge in Deep Scene CNNs Bolei Zhou A. Khosla Àgata Lapedriza A. Oliva Antonio Torralba ObjD 100 1,279 0 22 Dec 2014
Striving for Simplicity: The All Convolutional Net Jost Tobias Springenberg Alexey Dosovitskiy Thomas Brox Martin Riedmiller FAtt 144 4,653 0 21 Dec 2014
Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images Anh Totti Nguyen J. Yosinski Jeff Clune AAML 120 3,261 0 05 Dec 2014
Understanding Deep Image Representations by Inverting Them Aravindh Mahendran Andrea Vedaldi FAtt 79 1,959 0 26 Nov 2014
Going Deeper with Convolutions Christian Szegedy Wei Liu Yangqing Jia P. Sermanet Scott E. Reed Dragomir Anguelov D. Erhan Vincent Vanhoucke Andrew Rabinovich 269 43,511 0 17 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 952 39,383 0 01 Sep 2014
Microsoft COCO: Common Objects in Context Nayeon Lee Michael Maire Serge J. Belongie Lubomir Bourdev Ross B. Girshick James Hays Pietro Perona Deva Ramanan C. L. Zitnick Piotr Dollár ObjD 215 43,290 0 01 May 2014
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps Karen Simonyan Andrea Vedaldi Andrew Zisserman FAtt 133 7,252 0 20 Dec 2013
Visualizing and Understanding Convolutional Networks Matthew D. Zeiler Rob Fergus FAtt SSL 221 15,825 0 12 Nov 2013