Analysis of Explainers of Black Box Deep Neural Networks for Computer Vision: A Survey

27 November 2019

Papers citing "Analysis of Explainers of Black Box Deep Neural Networks for Computer Vision: A Survey"

20 / 70 papers shown

Title
Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model Benjamin Letham Cynthia Rudin Tyler H. McCormick D. Madigan FAtt 62 743 0 05 Nov 2015
Evaluating the visualization of what a Deep Neural Network has learned Wojciech Samek Alexander Binder G. Montavon Sebastian Lapuschkin K. Müller XAI 136 1,192 0 21 Sep 2015
Understanding Neural Networks Through Deep Visualization J. Yosinski Jeff Clune Anh Totti Nguyen Thomas J. Fuchs Hod Lipson FAtt AI4CE 122 1,872 0 22 Jun 2015
Inverting Visual Representations with Convolutional Networks Alexey Dosovitskiy Thomas Brox SSL FAtt 61 665 0 09 Jun 2015
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention Ke Xu Jimmy Ba Ryan Kiros Kyunghyun Cho Aaron Courville Ruslan Salakhutdinov R. Zemel Yoshua Bengio DiffM 334 10,069 0 10 Feb 2015
Striving for Simplicity: The All Convolutional Net Jost Tobias Springenberg Alexey Dosovitskiy Thomas Brox Martin Riedmiller FAtt 248 4,667 0 21 Dec 2014
Explaining and Harnessing Adversarial Examples Ian Goodfellow Jonathon Shlens Christian Szegedy AAML GAN 274 19,049 0 20 Dec 2014
Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images Anh Totti Nguyen J. Yosinski Jeff Clune AAML 158 3,271 0 05 Dec 2014
Understanding Deep Image Representations by Inverting Them Aravindh Mahendran Andrea Vedaldi FAtt 119 1,963 0 26 Nov 2014
Very Deep Convolutional Networks for Large-Scale Image Recognition Karen Simonyan Andrew Zisserman FAtt MDE 1.6K 100,348 0 04 Sep 2014
Analyzing the Performance of Multilayer Neural Networks for Object Recognition Pulkit Agrawal Ross B. Girshick Jitendra Malik SSL 107 444 0 07 Jul 2014
Methods and Models for Interpretable Linear Classification Berk Ustun Cynthia Rudin 87 44 0 16 May 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 413 43,638 0 01 May 2014
Intriguing properties of neural networks Christian Szegedy Wojciech Zaremba Ilya Sutskever Joan Bruna D. Erhan Ian Goodfellow Rob Fergus AAML 268 14,918 1 21 Dec 2013
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps Karen Simonyan Andrea Vedaldi Andrew Zisserman FAtt 312 7,292 0 20 Dec 2013
Visualizing and Understanding Convolutional Networks Matthew D. Zeiler Rob Fergus FAtt SSL 591 15,876 0 12 Nov 2013
Counterfactual Reasoning and Learning Systems Léon Bottou J. Peters J. Q. Candela Denis Xavier Charles D. M. Chickering Elon Portugaly Dipankar Ray Patrice Y. Simard Edward Snelson CML OffRL 385 783 0 11 Sep 2012
Multi-column Deep Neural Networks for Image Classification D. Ciresan U. Meier Jürgen Schmidhuber 162 3,939 0 13 Feb 2012
Natural Language Processing (almost) from Scratch R. Collobert Jason Weston Léon Bottou Michael Karlen Koray Kavukcuoglu Pavel P. Kuksa 186 7,725 0 02 Mar 2011
How to Explain Individual Classification Decisions D. Baehrens T. Schroeter Stefan Harmeling M. Kawanabe K. Hansen K. Müller FAtt 128 1,103 0 06 Dec 2009