W
Wojciech Samek
Researcher at Heinrich Hertz Institute
Publications - 90
Citations - 11059
Wojciech Samek is an academic researcher from Heinrich Hertz Institute. The author has contributed to research in topics: Computer science & Artificial neural network. The author has an hindex of 25, co-authored 59 publications receiving 7433 citations. Previous affiliations of Wojciech Samek include Fraunhofer Society & Technical University of Berlin.
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Journal ArticleDOI
On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation.
Sebastian Bach,Alexander Binder,Grégoire Montavon,Frederick Klauschen,Klaus-Robert Müller,Wojciech Samek +5 more
TL;DR: This work proposes a general solution to the problem of understanding classification decisions by pixel-wise decomposition of nonlinear classifiers by introducing a methodology that allows to visualize the contributions of single pixels to predictions for kernel-based classifiers over Bag of Words features and for multilayered neural networks.
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Methods for interpreting and understanding deep neural networks
Grégoire Montavon,Wojciech Samek,Klaus-Robert Müller,Klaus-Robert Müller,Klaus-Robert Müller +4 more
TL;DR: The second part of the tutorial focuses on the recently proposed layer-wise relevance propagation (LRP) technique, for which the author provides theory, recommendations, and tricks, to make most efficient use of it on real data.
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Explaining nonlinear classification decisions with deep Taylor decomposition
TL;DR: A novel methodology for interpreting generic multilayer neural networks by decomposing the network classification decision into contributions of its input elements by backpropagating the explanations from the output to the input layer is introduced.
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Evaluating the Visualization of What a Deep Neural Network Has Learned
TL;DR: In this article, a general methodology based on region perturbation for evaluating ordered collections of pixels such as heatmaps is presented, and the authors compare heatmaps computed by three different methods on the SUN397, ILSVRC2012, and MIT Places data sets.
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Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment
TL;DR: A deep neural network-based approach to image quality assessment (IQA) that allows for joint learning of local quality and local weights in an unified framework and shows a high ability to generalize between different databases, indicating a high robustness of the learned features.