SmoothGrad: removing noise by adding noise

12 June 2017

Papers citing "SmoothGrad: removing noise by adding noise"

50 / 1,161 papers shown

Title
NeuroMask: Explaining Predictions of Deep Neural Networks through Mask Learning M. Alzantot Amy Widdicombe S. Julier Mani B. Srivastava AAML FAtt 20 3 0 05 Aug 2019
Semi-supervised Thai Sentence Segmentation Using Local and Distant Word Representations Chanatip Saetia E. Chuangsuwanich Tawunrat Chalothorn P. Vateekul 15 5 0 04 Aug 2019
Smooth Grad-CAM++: An Enhanced Inference Level Visualization Technique for Deep Convolutional Neural Network Models Daniel Omeiza Skyler Speakman C. Cintas Komminist Weldemariam FAtt 22 216 0 03 Aug 2019
Grid Saliency for Context Explanations of Semantic Segmentation Lukas Hoyer Mauricio Muñoz P. Katiyar Anna Khoreva Volker Fischer FAtt 25 48 0 30 Jul 2019
explAIner: A Visual Analytics Framework for Interactive and Explainable Machine Learning Thilo Spinner U. Schlegel H. Schäfer Mennatallah El-Assady HAI 15 234 0 29 Jul 2019
How to Manipulate CNNs to Make Them Lie: the GradCAM Case T. Viering Ziqi Wang Marco Loog E. Eisemann AAML FAtt 17 28 0 25 Jul 2019
Benchmarking Attribution Methods with Relative Feature Importance Mengjiao Yang Been Kim FAtt XAI 21 140 0 23 Jul 2019
Information-Bottleneck Approach to Salient Region Discovery A. Zhmoginov Ian S. Fischer Mark Sandler 12 18 0 22 Jul 2019
A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAI Erico Tjoa Cuntai Guan XAI 56 1,413 0 17 Jul 2019
Explaining Classifiers with Causal Concept Effect (CaCE) Yash Goyal Amir Feder Uri Shalit Been Kim CML 19 172 0 16 Jul 2019
Explaining an increase in predicted risk for clinical alerts Michaela Hardt A. Rajkomar Gerardo Flores Andrew M. Dai M. Howell Greg S. Corrado Claire Cui Moritz Hardt FAtt 17 12 0 10 Jul 2019
The What-If Tool: Interactive Probing of Machine Learning Models James Wexler Mahima Pushkarna Tolga Bolukbasi Martin Wattenberg F. Viégas Jimbo Wilson VLM 57 484 0 09 Jul 2019
ELF: Embedded Localisation of Features in pre-trained CNN Assia Benbihi M. Geist C´edric Pradalier 19 30 0 07 Jul 2019
Towards Robust, Locally Linear Deep Networks Guang-He Lee David Alvarez-Melis Tommi Jaakkola ODL 19 48 0 07 Jul 2019
On the Privacy Risks of Model Explanations Reza Shokri Martin Strobel Yair Zick MIACV PILM SILM FAtt 6 36 0 29 Jun 2019
Improving performance of deep learning models with axiomatic attribution priors and expected gradients G. Erion Joseph D. Janizek Pascal Sturmfels Scott M. Lundberg Su-In Lee OOD BDL FAtt 21 80 0 25 Jun 2019
Saliency-driven Word Alignment Interpretation for Neural Machine Translation Shuoyang Ding Hainan Xu Philipp Koehn 20 55 0 25 Jun 2019
Incorporating Priors with Feature Attribution on Text Classification Frederick Liu Besim Avci FAtt FaML 31 120 0 19 Jun 2019
Explanations can be manipulated and geometry is to blame Ann-Kathrin Dombrowski Maximilian Alber Christopher J. Anders M. Ackermann K. Müller Pan Kessel AAML FAtt 22 329 0 19 Jun 2019
Exact and Consistent Interpretation of Piecewise Linear Models Hidden behind APIs: A Closed Form Solution Zicun Cong Lingyang Chu Lanjun Wang X. Hu J. Pei 197 5 0 17 Jun 2019
Issues with post-hoc counterfactual explanations: a discussion Thibault Laugel Marie-Jeanne Lesot Christophe Marsala Marcin Detyniecki CML 107 44 0 11 Jun 2019
XRAI: Better Attributions Through Regions A. Kapishnikov Tolga Bolukbasi Fernanda Viégas Michael Terry FAtt XAI 20 212 0 06 Jun 2019
c-Eval: A Unified Metric to Evaluate Feature-based Explanations via Perturbation Minh Nhat Vu Truc D. T. Nguyen Nhathai Phan Ralucca Gera My T. Thai AAML FAtt 20 22 0 05 Jun 2019
Interpretable and Differentially Private Predictions Frederik Harder Matthias Bauer Mijung Park FAtt 6 52 0 05 Jun 2019
Adversarial Robustness as a Prior for Learned Representations Logan Engstrom Andrew Ilyas Shibani Santurkar Dimitris Tsipras Brandon Tran A. Madry OOD AAML 27 63 0 03 Jun 2019
Explainability Techniques for Graph Convolutional Networks Federico Baldassarre Hossein Azizpour GNN FAtt 22 264 0 31 May 2019
Learning Representations by Humans, for Humans Sophie Hilgard Nir Rosenfeld M. Banaji Jack Cao David C. Parkes OCL HAI AI4CE 34 29 0 29 May 2019
Certifiably Robust Interpretation in Deep Learning Alexander Levine Sahil Singla S. Feizi FAtt AAML 26 63 0 28 May 2019
A Rate-Distortion Framework for Explaining Neural Network Decisions Jan Macdonald S. Wäldchen Sascha Hauch Gitta Kutyniok 13 40 0 27 May 2019
Interpreting Adversarially Trained Convolutional Neural Networks Tianyuan Zhang Zhanxing Zhu AAML GAN FAtt 28 157 0 23 May 2019
What Do Adversarially Robust Models Look At? Takahiro Itazuri Yoshihiro Fukuhara Hirokatsu Kataoka Shigeo Morishima 19 5 0 19 May 2019
On the Connection Between Adversarial Robustness and Saliency Map Interpretability Christian Etmann Sebastian Lunz Peter Maass Carola-Bibiane Schönlieb AAML FAtt 15 156 0 10 May 2019
Embedding Human Knowledge into Deep Neural Network via Attention Map Masahiro Mitsuhara Hiroshi Fukui Yusuke Sakashita Takanori Ogata Tsubasa Hirakawa Takayoshi Yamashita H. Fujiyoshi 16 72 0 09 May 2019
Adversarial Examples Are Not Bugs, They Are Features Andrew Ilyas Shibani Santurkar Dimitris Tsipras Logan Engstrom Brandon Tran A. Madry SILM 28 1,807 0 06 May 2019
Temporal Graph Convolutional Networks for Automatic Seizure Detection Ian Covert B. Krishnan I. Najm Jiening Zhan Matthew Shore J. Hixson M. Po 8 67 0 03 May 2019
Full-Gradient Representation for Neural Network Visualization Suraj Srinivas F. Fleuret MILM FAtt 21 268 0 02 May 2019
Dropping Pixels for Adversarial Robustness Hossein Hosseini Sreeram Kannan Radha Poovendran 14 16 0 01 May 2019
Beyond Explainability: Leveraging Interpretability for Improved Adversarial Learning Devinder Kumar Ibrahim Ben Daya Kanav Vats Jeffery Feng Graham W. Taylor Alexander Wong AAML 14 1 0 21 Apr 2019
Software and application patterns for explanation methods Maximilian Alber 33 11 0 09 Apr 2019
Visualization of Convolutional Neural Networks for Monocular Depth Estimation Junjie Hu Yan Zhang Takayuki Okatani MDE 33 83 0 06 Apr 2019
Summit: Scaling Deep Learning Interpretability by Visualizing Activation and Attribution Summarizations Fred Hohman Haekyu Park Caleb Robinson Duen Horng Chau FAtt 3DH HAI 19 213 0 04 Apr 2019
InfoMask: Masked Variational Latent Representation to Localize Chest Disease Saeid Asgari Taghanaki Mohammad Havaei T. Berthier Francis Dutil Lisa Di-Jorio Ghassan Hamarneh Yoshua Bengio 15 42 0 28 Mar 2019
Bridging Adversarial Robustness and Gradient Interpretability Beomsu Kim Junghoon Seo Taegyun Jeon AAML 16 39 0 27 Mar 2019
Activation Analysis of a Byte-Based Deep Neural Network for Malware Classification Scott E. Coull Christopher Gardner 16 50 0 12 Mar 2019
Aggregating explanation methods for stable and robust explainability Laura Rieger Lars Kai Hansen AAML FAtt 37 11 0 01 Mar 2019
Explaining a black-box using Deep Variational Information Bottleneck Approach Seo-Jin Bang P. Xie Heewook Lee Wei Wu Eric Xing XAI FAtt 14 75 0 19 Feb 2019
Regularizing Black-box Models for Improved Interpretability Gregory Plumb Maruan Al-Shedivat Ángel Alexander Cabrera Adam Perer Eric Xing Ameet Talwalkar AAML 24 79 0 18 Feb 2019
Why are Saliency Maps Noisy? Cause of and Solution to Noisy Saliency Maps Beomsu Kim Junghoon Seo Seunghyun Jeon Jamyoung Koo J. Choe Taegyun Jeon FAtt 32 69 0 13 Feb 2019
Certified Adversarial Robustness via Randomized Smoothing Jeremy M. Cohen Elan Rosenfeld J. Zico Kolter AAML 17 1,992 0 08 Feb 2019
Towards Automatic Concept-based Explanations Amirata Ghorbani James Wexler James Zou Been Kim FAtt LRM 38 19 0 07 Feb 2019