1k citations · 1.1k across the 2 of their papers we have counts for
5 papers
Towards Best Practice in Explaining Neural Network Decisions with LRP
Maximilian Kohlbrenner, Alexander Bauer, Shinichi Nakajima +3
Within the last decade, neural network based predictors have demonstrated impressive - and at times super-human - capabilities. This performance is often paid for with an intranspa…
Resolving challenges in deep learning-based analyses of histopathological images using explanation methods
Miriam Hägele, Philipp Seegerer, Sebastian Lapuschkin +5
Deep learning has recently gained popularity in digital pathology due to its high prediction quality. However, the medical domain requires explanation and insight for a better unde…
Unmasking Clever Hans Predictors and Assessing What Machines Really Learn
Sebastian Lapuschkin, Stephan Wäldchen, Alexander Binder +3
Current learning machines have successfully solved hard application problems, reaching high accuracy and displaying seemingly "intelligent" behavior. Here we apply recent technique…
Understanding and Comparing Deep Neural Networks for Age and Gender Classification
Sebastian Lapuschkin, Alexander Binder, Klaus-Robert Müller +1
Recently, deep neural networks have demonstrated excellent performances in recognizing the age and gender on human face images. However, these models were applied in a black-box ma…
Layer-wise Relevance Propagation for Neural Networks with Local Renormalization Layers
Alexander Binder, Grégoire Montavon, Sebastian Bach +2
Layer-wise relevance propagation is a framework which allows to decompose the prediction of a deep neural network computed over a sample, e.g. an image, down to relevance scores fo…