Full-Gradient Representation for Neural Network Visualization

NeurIPS 2019 Suraj SrinivasFrancois Fleuret

We introduce a new tool for interpreting neural net responses, namely full-gradients, which decomposes the neural net response into input sensitivity and per-neuron sensitivity components. This is the first proposed representation which satisfies two key properties: completeness and weak dependence, which provably cannot be satisfied by any saliency map-based interpretability method... (read more)

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