A new explanation method for ReLU neural networks that builds piecewise linear models from selected connected subsets of hidden units, called paths, to produce adjustable and decomposable input attributions.
On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
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Pathwise Explanation of ReLU Neural Networks
A new explanation method for ReLU neural networks that builds piecewise linear models from selected connected subsets of hidden units, called paths, to produce adjustable and decomposable input attributions.