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Found 6 papers, 2 papers with code
Interpretable Deep Neural Networks for Single-Trial EEG Classification
With LRP a new quality of high-resolution assessment of neural activity can be reached.
Layer-wise Relevance Propagation for Neural Networks with Local Renormalization Layers
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 for the single input dimensions of the sample such as subpixels of an image.
Controlling Explanatory Heatmap Resolution and Semantics via Decomposition Depth
We present an application of the Layer-wise Relevance Propagation (LRP) algorithm to state of the art deep convolutional neural networks and Fisher Vector classifiers to compare the image perception and prediction strategies of both classifiers with the use of visualized heatmaps.
Explaining NonLinear Classification Decisions with Deep Taylor Decomposition
Although our focus is on image classification, the method is applicable to a broad set of input data, learning tasks and network architectures.
Analyzing Classifiers: Fisher Vectors and Deep Neural Networks
Fisher Vector classifiers and Deep Neural Networks (DNNs) are popular and successful algorithms for solving image classification problems.
Evaluating the visualization of what a Deep Neural Network has learned
Our main result is that the recently proposed Layer-wise Relevance Propagation (LRP) algorithm qualitatively and quantitatively provides a better explanation of what made a DNN arrive at a particular classification decision than the sensitivity-based approach or the deconvolution method.
