SmoothGrad: removing noise by adding noise

SmoothGrad: removing noise by adding noise

12 June 2017

Martin Wattenberg

Papers citing "SmoothGrad: removing noise by adding noise"

11 / 1,161 papers shown

Title
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 18 241 0 02 Feb 2018
Training Set Debugging Using Trusted Items Xuezhou Zhang Xiaojin Zhu Stephen J. Wright 11 73 0 24 Jan 2018
Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontiers Fred Hohman Minsuk Kahng Robert S. Pienta Duen Horng Chau OOD HAI 30 536 0 21 Jan 2018
Beyond saliency: understanding convolutional neural networks from saliency prediction on layer-wise relevance propagation Heyi Li Yunke Tian Klaus Mueller Xin Chen FAtt 19 38 0 22 Dec 2017
A Perceptual Measure for Deep Single Image Camera Calibration Yannick Hold-Geoffroy Kalyan Sunkavalli Jonathan Eisenmann Matt Fisher Emiliano Gambaretto Sunil Hadap Jean-François Lalonde 3DV 16 106 0 02 Dec 2017
Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV) Been Kim Martin Wattenberg Justin Gilmer Carrie J. Cai James Wexler F. Viégas Rory Sayres FAtt 77 1,791 0 30 Nov 2017
Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients A. Ross Finale Doshi-Velez AAML 26 675 0 26 Nov 2017
No Classification without Representation: Assessing Geodiversity Issues in Open Data Sets for the Developing World S. Shankar Yoni Halpern Eric Breck James Atwood Jimbo Wilson D. Sculley 11 287 0 22 Nov 2017
Towards better understanding of gradient-based attribution methods for Deep Neural Networks Marco Ancona Enea Ceolini Cengiz Öztireli Markus Gross FAtt 19 145 0 16 Nov 2017
The (Un)reliability of saliency methods Pieter-Jan Kindermans Sara Hooker Julius Adebayo Maximilian Alber Kristof T. Schütt Sven Dähne D. Erhan Been Kim FAtt XAI 31 678 0 02 Nov 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 16 338 0 16 May 2017