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"

50 / 70 papers shown

Title
Distilling Machine Learning's Added Value: Pareto Fronts in Atmospheric Applications Tom Beucler Arthur Grundner Sara Shamekh Peter Ukkonen Matthew Chantry Ryan Lagerquist 73 0 0 04 Aug 2024
On Neural Networks as Infinite Tree-Structured Probabilistic Graphical Models Yue Liu Alexandar J. Thomson Matthew M. Engelhard David Page David Page BDL AI4CE 160 0 0 27 May 2023
A Survey on the Explainability of Supervised Machine Learning Nadia Burkart Marco F. Huber FaML XAI 48 773 0 16 Nov 2020
Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey Arun Das P. Rad XAI 152 602 0 16 Jun 2020
An Investigation of COVID-19 Spreading Factors with Explainable AI Techniques Xiuyi Fan Siyuan Liu Jiarong Chen T. Henderson 30 7 0 05 May 2020
DeepCOVIDExplainer: Explainable COVID-19 Diagnosis Based on Chest X-ray Images Md. Rezaul Karim Till Dohmen Dietrich-Rebholz Schuhmann Stefan Decker Michael Cochez Oya Beyan 53 88 0 09 Apr 2020
Attacking Optical Flow Anurag Ranjan J. Janai Andreas Geiger Michael J. Black AAML 3DPC 60 87 0 22 Oct 2019
Understanding Deep Networks via Extremal Perturbations and Smooth Masks Ruth C. Fong Mandela Patrick Andrea Vedaldi AAML 66 415 0 18 Oct 2019
Summit: Scaling Deep Learning Interpretability by Visualizing Activation and Attribution Summarizations Fred Hohman Haekyu Park Caleb Robinson Duen Horng Chau FAtt 3DH HAI 39 217 0 04 Apr 2019
On the (In)fidelity and Sensitivity for Explanations Chih-Kuan Yeh Cheng-Yu Hsieh A. Suggala David I. Inouye Pradeep Ravikumar FAtt 58 453 0 27 Jan 2019
Equalizing Gender Biases in Neural Machine Translation with Word Embeddings Techniques Joel Escudé Font Marta R. Costa-jussá 53 170 0 10 Jan 2019
ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness Robert Geirhos Patricia Rubisch Claudio Michaelis Matthias Bethge Felix Wichmann Wieland Brendel 100 2,668 0 29 Nov 2018
A Benchmark for Interpretability Methods in Deep Neural Networks Sara Hooker D. Erhan Pieter-Jan Kindermans Been Kim FAtt UQCV 105 681 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 86 1,858 0 31 May 2018
Adversarial Attacks on Face Detectors using Neural Net based Constrained Optimization A. Bose P. Aarabi AAML 40 89 0 31 May 2018
Multimodal Explanations: Justifying Decisions and Pointing to the Evidence Dong Huk Park Lisa Anne Hendricks Zeynep Akata Anna Rohrbach Bernt Schiele Trevor Darrell Marcus Rohrbach 73 421 0 15 Feb 2018
A Survey Of Methods For Explaining Black Box Models Riccardo Guidotti A. Monreale Salvatore Ruggieri Franco Turini D. Pedreschi F. Giannotti XAI 124 3,957 0 06 Feb 2018
How do Humans Understand Explanations from Machine Learning Systems? An Evaluation of the Human-Interpretability of Explanation Menaka Narayanan Emily Chen Jeffrey He Been Kim S. Gershman Finale Doshi-Velez FAtt XAI 99 242 0 02 Feb 2018
A General Framework for Adversarial Examples with Objectives Mahmood Sharif Sruti Bhagavatula Lujo Bauer Michael K. Reiter AAML GAN 51 193 0 31 Dec 2017
An Introduction to Deep Visual Explanation H. Babiker Randy Goebel FAtt AAML 52 19 0 26 Nov 2017
Using KL-divergence to focus Deep Visual Explanation H. Babiker Randy Goebel FAtt 54 12 0 17 Nov 2017
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks Aditya Chattopadhyay Anirban Sarkar Prantik Howlader V. Balasubramanian FAtt 106 2,297 0 30 Oct 2017
Adversarial Examples for Evaluating Reading Comprehension Systems Robin Jia Percy Liang AAML ELM 196 1,605 0 23 Jul 2017
SmoothGrad: removing noise by adding noise D. Smilkov Nikhil Thorat Been Kim F. Viégas Martin Wattenberg FAtt ODL 201 2,221 0 12 Jun 2017
A Unified Approach to Interpreting Model Predictions Scott M. Lundberg Su-In Lee FAtt 1.1K 21,906 0 22 May 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 73 339 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 1,516 1 19 Apr 2017
Interpretable Explanations of Black Boxes by Meaningful Perturbation Ruth C. Fong Andrea Vedaldi FAtt AAML 74 1,519 0 11 Apr 2017
Learning Important Features Through Propagating Activation Differences Avanti Shrikumar Peyton Greenside A. Kundaje FAtt 198 3,871 0 10 Apr 2017
Learning to Generate Reviews and Discovering Sentiment Alec Radford Rafal Jozefowicz Ilya Sutskever 93 509 0 05 Apr 2017
Axiomatic Attribution for Deep Networks Mukund Sundararajan Ankur Taly Qiqi Yan OOD FAtt 182 5,986 0 04 Mar 2017
Opening the Black Box of Deep Neural Networks via Information Ravid Shwartz-Ziv Naftali Tishby AI4CE 98 1,409 0 02 Mar 2017
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 399 3,787 0 28 Feb 2017
Visualizing Deep Neural Network Decisions: Prediction Difference Analysis L. Zintgraf Taco S. Cohen T. Adel Max Welling FAtt 132 708 0 15 Feb 2017
TreeView: Peeking into Deep Neural Networks Via Feature-Space Partitioning Jayaraman J. Thiagarajan B. Kailkhura P. Sattigeri Karthikeyan N. Ramamurthy 54 38 0 22 Nov 2016
VisualBackProp: efficient visualization of CNNs Mariusz Bojarski A. Choromańska K. Choromanski Bernhard Firner L. Jackel Urs Muller Karol Zieba FAtt 65 74 0 16 Nov 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 297 20,003 0 07 Oct 2016
RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism Edward Choi M. T. Bahadori Joshua A. Kulas A. Schuetz Walter F. Stewart Jimeng Sun AI4TS 115 1,245 0 19 Aug 2016
Top-down Neural Attention by Excitation Backprop Jianming Zhang Zhe Lin Jonathan Brandt Xiaohui Shen Stan Sclaroff 79 947 0 01 Aug 2016
Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings Tolga Bolukbasi Kai-Wei Chang James Zou Venkatesh Saligrama Adam Kalai CVBM FaML 107 3,135 0 21 Jul 2016
European Union regulations on algorithmic decision-making and a "right to explanation" B. Goodman Seth Flaxman FaML AILaw 63 1,900 0 28 Jun 2016
Rationalizing Neural Predictions Tao Lei Regina Barzilay Tommi Jaakkola 110 812 0 13 Jun 2016
The Mythos of Model Interpretability Zachary Chase Lipton FaML 180 3,699 0 10 Jun 2016
The Latin American Giant Observatory: a successful collaboration in Latin America based on Cosmic Rays and computer science domains Hernán Asorey R. Mayo-García L. Núñez M. Pascual A. J. Rubio-Montero M. Suárez-Durán L. A. Torres-Niño 81 5 0 30 May 2016
Interpretable Deep Neural Networks for Single-Trial EEG Classification I. Sturm Sebastian Bach Wojciech Samek K. Müller 58 353 0 27 Apr 2016
Colorful Image Colorization Richard Y. Zhang Phillip Isola Alexei A. Efros 127 3,529 0 28 Mar 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 1.2K 16,976 0 16 Feb 2016
Learning Deep Features for Discriminative Localization Bolei Zhou A. Khosla Àgata Lapedriza A. Oliva Antonio Torralba SSL SSeg FAtt 250 9,308 0 14 Dec 2015
Deep Residual Learning for Image Recognition Kaiming He Xinming Zhang Shaoqing Ren Jian Sun MedIm 2.2K 193,878 0 10 Dec 2015
DeepFool: a simple and accurate method to fool deep neural networks Seyed-Mohsen Moosavi-Dezfooli Alhussein Fawzi P. Frossard AAML 148 4,895 0 14 Nov 2015