Breaking Batch Normalization for better explainability of Deep Neural Networks through Layer-wise Relevance Propagation

24 February 2020

Papers citing "Breaking Batch Normalization for better explainability of Deep Neural Networks through Layer-wise Relevance Propagation"

4 / 4 papers shown

Title
Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models Wojciech Samek Thomas Wiegand K. Müller XAI VLM 53 1,186 0 28 Aug 2017
Learning Important Features Through Propagating Activation Differences Avanti Shrikumar Peyton Greenside A. Kundaje FAtt 121 3,848 0 10 Apr 2017
WaveNet: A Generative Model for Raw Audio Aaron van den Oord Sander Dieleman Heiga Zen Karen Simonyan Oriol Vinyals Alex Graves Nal Kalchbrenner A. Senior Koray Kavukcuoglu DiffM 280 7,361 0 12 Sep 2016
The Mythos of Model Interpretability Zachary Chase Lipton FaML 118 3,672 0 10 Jun 2016