Contents
What are LRP rules?
Layer-wise Relevance Propagation (LRP) is a technique that brings such explainability and scales to potentially highly complex deep neural networks. It operates by propagating the prediction backward in the neural network, using a set of purposely designed propagation rules.
What is LRP algorithm?
The Layer-wise Relevance Propagation (LRP) algorithm explains a classifier’s prediction specific to a given data point by attributing relevance scores to important components of the input by using the topology of the learned model itself.
What is LRP in machine learning?
Layer-wise Relevance Propagation (LRP) is one of the most prominent methods in explainable machine learning (XML). XML methods are especially useful in safety-critical domains where practitioners must know exactly what the network is paying attention to.
What is deep Taylor decomposition?
Specifically, we view each neuron of a deep network as a function that can be expanded and decomposed on its input variables. The decompositions of multiple neurons are then aggregated or propagated backwards, resulting in a “deep Taylor decomposition”.
What integrated gradients?
What is Integrated Gradient? Integrated Gradient(IG) computes the gradient of the model’s prediction output to its input features and requires no modification to the original deep neural network. IG can be applied to any differentiable model like image, text, or structured data.
How to decompose LRP and deep Taylor decomposition?
LRP and Deep Taylor Decomposition Wojciech Samek, Klaus-Robert Müller, Gregoire Montavon et al. Interpretability by Decomposition 2 Decomposing the Gradient Norm 3 4 Decomposing the Gradient Norm 5 Decomposing the Prediction 6 Decomposing the Prediction 7 Decomposing the Prediction 8 Simple Taylor in Practice
What are the propagation rules used by LRP?
The propagation rules used by LRP can for many architectures, including deep rectifier networks or LSTMs, be understood as a Deep Taylor Decomposition of the prediction. W Samek, L Arras, A Osman, G Montavon, KR Müller.
What is the goal of the LRP technique?
The technique was originally described in this paper. The goal of LRP is to define some relevance measure R over the input vector such that we can express the network output as the sum of the values of R:
Which is the best neural network for LRP?
MNIST: A simple LRP demo based on a neural network that predicts handwritten digits and was trained using the MNIST data set. Caffe: A more complex LRP demo based on a neural network implemented using Caffe. The neural network predicts the contents of the picture.