A Benchmark for Interpretability Methods in Deep Neural Networks

28 June 2018

Papers citing "A Benchmark for Interpretability Methods in Deep Neural Networks"

8 / 108 papers shown

Title
Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications Wojciech Samek G. Montavon Sebastian Lapuschkin Christopher J. Anders K. Müller XAI 36 82 0 17 Mar 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
Selectivity considered harmful: evaluating the causal impact of class selectivity in DNNs Matthew L. Leavitt Ari S. Morcos 50 33 0 03 Mar 2020
Identifying Critical Neurons in ANN Architectures using Mixed Integer Programming M. Elaraby Guy Wolf Margarida Carvalho 26 5 0 17 Feb 2020
Explaining Explanations: Axiomatic Feature Interactions for Deep Networks Joseph D. Janizek Pascal Sturmfels Su-In Lee FAtt 20 143 0 10 Feb 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
Revisiting the Importance of Individual Units in CNNs via Ablation Bolei Zhou Yiyou Sun David Bau Antonio Torralba FAtt 54 116 0 07 Jun 2018