Sanity Checks for Saliency Maps

8 October 2018

Justin Gilmer

Papers citing "Sanity Checks for Saliency Maps"

50 / 302 papers shown

Title
Learning Propagation Rules for Attribution Map Generation Yiding Yang Jiayan Qiu Mingli Song Dacheng Tao Xinchao Wang FAtt 30 17 0 14 Oct 2020
Visualizing Color-wise Saliency of Black-Box Image Classification Models Yuhki Hatakeyama Hiroki Sakuma Yoshinori Konishi Kohei Suenaga FAtt 14 3 0 06 Oct 2020
Remembering for the Right Reasons: Explanations Reduce Catastrophic Forgetting Sayna Ebrahimi Suzanne Petryk Akash Gokul William Gan Joseph E. Gonzalez Marcus Rohrbach Trevor Darrell CLL 16 45 0 04 Oct 2020
Explaining Deep Neural Networks Oana-Maria Camburu XAI FAtt 20 26 0 04 Oct 2020
Trustworthy Convolutional Neural Networks: A Gradient Penalized-based Approach Nicholas F Halliwell Freddy Lecue FAtt 17 9 0 29 Sep 2020
Quantitative and Qualitative Evaluation of Explainable Deep Learning Methods for Ophthalmic Diagnosis Amitojdeep Singh J. Balaji M. Rasheed Varadharajan Jayakumar R. Raman Vasudevan Lakshminarayanan BDL XAI FAtt 4 29 0 26 Sep 2020
What Do You See? Evaluation of Explainable Artificial Intelligence (XAI) Interpretability through Neural Backdoors Yi-Shan Lin Wen-Chuan Lee Z. Berkay Celik XAI 24 93 0 22 Sep 2020
Captum: A unified and generic model interpretability library for PyTorch Narine Kokhlikyan Vivek Miglani Miguel Martin Edward Wang B. Alsallakh ... Alexander Melnikov Natalia Kliushkina Carlos Araya Siqi Yan Orion Reblitz-Richardson FAtt 17 821 0 16 Sep 2020
Model extraction from counterfactual explanations Ulrich Aivodji Alexandre Bolot Sébastien Gambs MIACV MLAU 25 51 0 03 Sep 2020
Deep Learning in Protein Structural Modeling and Design Wenhao Gao S. Mahajan Jeremias Sulam Jeffrey J. Gray 21 159 0 16 Jul 2020
A simple defense against adversarial attacks on heatmap explanations Laura Rieger Lars Kai Hansen FAtt AAML 25 37 0 13 Jul 2020
Drug discovery with explainable artificial intelligence José Jiménez-Luna F. Grisoni G. Schneider 25 625 0 01 Jul 2020
Adversarial Infidelity Learning for Model Interpretation Jian Liang Bing Bai Yuren Cao Kun Bai Fei-Yue Wang AAML 38 18 0 09 Jun 2020
Higher-Order Explanations of Graph Neural Networks via Relevant Walks Thomas Schnake Oliver Eberle Jonas Lederer Shinichi Nakajima Kristof T. Schütt Klaus-Robert Muller G. Montavon 21 215 0 05 Jun 2020
Local and Global Explanations of Agent Behavior: Integrating Strategy Summaries with Saliency Maps Tobias Huber Katharina Weitz Elisabeth André Ofra Amir FAtt 16 63 0 18 May 2020
Evaluating and Aggregating Feature-based Model Explanations Umang Bhatt Adrian Weller J. M. F. Moura XAI 28 218 0 01 May 2020
Explainable Deep Learning: A Field Guide for the Uninitiated Gabrielle Ras Ning Xie Marcel van Gerven Derek Doran AAML XAI 29 371 0 30 Apr 2020
Generating Fact Checking Explanations Pepa Atanasova J. Simonsen Christina Lioma Isabelle Augenstein 6 188 0 13 Apr 2020
Ground Truth Evaluation of Neural Network Explanations with CLEVR-XAI L. Arras Ahmed Osman Wojciech Samek XAI AAML 21 149 0 16 Mar 2020
Measuring and improving the quality of visual explanations Agnieszka Grabska-Barwiñska XAI FAtt 6 3 0 14 Mar 2020
IROF: a low resource evaluation metric for explanation methods Laura Rieger Lars Kai Hansen 15 55 0 09 Mar 2020
Causal Interpretability for Machine Learning -- Problems, Methods and Evaluation Raha Moraffah Mansooreh Karami Ruocheng Guo A. Raglin Huan Liu CML ELM XAI 18 212 0 09 Mar 2020
Gradient-Adjusted Neuron Activation Profiles for Comprehensive Introspection of Convolutional Speech Recognition Models A. Krug Sebastian Stober 14 0 0 19 Feb 2020
Explaining Explanations: Axiomatic Feature Interactions for Deep Networks Joseph D. Janizek Pascal Sturmfels Su-In Lee FAtt 22 143 0 10 Feb 2020
Concept Whitening for Interpretable Image Recognition Zhi Chen Yijie Bei Cynthia Rudin FAtt 12 313 0 05 Feb 2020
GraphLIME: Local Interpretable Model Explanations for Graph Neural Networks Q. Huang M. Yamada Yuan Tian Dinesh Singh Dawei Yin Yi-Ju Chang FAtt 26 344 0 17 Jan 2020
Making deep neural networks right for the right scientific reasons by interacting with their explanations P. Schramowski Wolfgang Stammer Stefano Teso Anna Brugger Xiaoting Shao Hans-Georg Luigs Anne-Katrin Mahlein Kristian Kersting 13 207 0 15 Jan 2020
On Interpretability of Artificial Neural Networks: A Survey Fenglei Fan Jinjun Xiong Mengzhou Li Ge Wang AAML AI4CE 30 300 0 08 Jan 2020
When Explanations Lie: Why Many Modified BP Attributions Fail Leon Sixt Maximilian Granz Tim Landgraf BDL FAtt XAI 11 132 0 20 Dec 2019
On the Explanation of Machine Learning Predictions in Clinical Gait Analysis D. Slijepcevic Fabian Horst Sebastian Lapuschkin Anna-Maria Raberger Matthias Zeppelzauer Wojciech Samek C. Breiteneder W. Schöllhorn B. Horsak 22 50 0 16 Dec 2019
CXPlain: Causal Explanations for Model Interpretation under Uncertainty Patrick Schwab W. Karlen FAtt CML 29 205 0 27 Oct 2019
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI Alejandro Barredo Arrieta Natalia Díaz Rodríguez Javier Del Ser Adrien Bennetot S. Tabik ... S. Gil-Lopez Daniel Molina Richard Benjamins Raja Chatila Francisco Herrera XAI 37 6,109 0 22 Oct 2019
Understanding Deep Networks via Extremal Perturbations and Smooth Masks Ruth C. Fong Mandela Patrick Andrea Vedaldi AAML 22 411 0 18 Oct 2019
Can I Trust the Explainer? Verifying Post-hoc Explanatory Methods Oana-Maria Camburu Eleonora Giunchiglia Jakob N. Foerster Thomas Lukasiewicz Phil Blunsom FAtt AAML 21 59 0 04 Oct 2019
Visual Explanation for Deep Metric Learning Sijie Zhu Taojiannan Yang C. L. P. Chen FAtt 22 33 0 27 Sep 2019
Deep Weakly-Supervised Learning Methods for Classification and Localization in Histology Images: A Survey Jérôme Rony Soufiane Belharbi Jose Dolz Ismail Ben Ayed Luke McCaffrey Eric Granger 23 70 0 08 Sep 2019
Saccader: Improving Accuracy of Hard Attention Models for Vision Gamaleldin F. Elsayed Simon Kornblith Quoc V. Le VLM 25 70 0 20 Aug 2019
Visual Interaction with Deep Learning Models through Collaborative Semantic Inference Sebastian Gehrmann Hendrik Strobelt Robert Krüger Hanspeter Pfister Alexander M. Rush HAI 13 57 0 24 Jul 2019
AlphaStock: A Buying-Winners-and-Selling-Losers Investment Strategy using Interpretable Deep Reinforcement Attention Networks Jingyuan Wang Yang Zhang Ke Tang Junjie Wu Zhang Xiong AIFin 11 119 0 24 Jul 2019
Graph Neural Network for Interpreting Task-fMRI Biomarkers Xiaoxiao Li Nicha Dvornek Yuan Zhou Juntang Zhuang P. Ventola James S. Duncan 25 101 0 02 Jul 2019
Incorporating Priors with Feature Attribution on Text Classification Frederick Liu Besim Avci FAtt FaML 23 120 0 19 Jun 2019
The Secrets of Machine Learning: Ten Things You Wish You Had Known Earlier to be More Effective at Data Analysis Cynthia Rudin David Carlson HAI 9 34 0 04 Jun 2019
Adversarial Robustness as a Prior for Learned Representations Logan Engstrom Andrew Ilyas Shibani Santurkar Dimitris Tsipras Brandon Tran A. Madry OOD AAML 11 63 0 03 Jun 2019
Certifiably Robust Interpretation in Deep Learning Alexander Levine Sahil Singla S. Feizi FAtt AAML 13 63 0 28 May 2019
Interpreting Adversarially Trained Convolutional Neural Networks Tianyuan Zhang Zhanxing Zhu AAML GAN FAtt 17 157 0 23 May 2019
What Do Adversarially Robust Models Look At? Takahiro Itazuri Yoshihiro Fukuhara Hirokatsu Kataoka Shigeo Morishima 11 5 0 19 May 2019
Towards Automatic Concept-based Explanations Amirata Ghorbani James Wexler James Zou Been Kim FAtt LRM 22 19 0 07 Feb 2019
Interpretable machine learning: definitions, methods, and applications W. James Murdoch Chandan Singh Karl Kumbier R. Abbasi-Asl Bin-Xia Yu XAI HAI 21 1,416 0 14 Jan 2019
Context-encoding Variational Autoencoder for Unsupervised Anomaly Detection David Zimmerer Simon A. A. Kohl Jens Petersen Fabian Isensee Klaus H. Maier-Hein DRL 19 128 0 14 Dec 2018
Interpretable Deep Learning under Fire Xinyang Zhang Ningfei Wang Hua Shen S. Ji Xiapu Luo Ting Wang AAML AI4CE 11 168 0 03 Dec 2018