Source: Deep Learning on Medium

# A Review of Different Interpretation Methods in Deep Learning (Part 2: Input × Gradient, Layerwise Relevance Propagation, DeepLIFT, LIME)[In progress…]

Welcome to the second article of the series of “A Review of Different Interpretation Methods in Deep Learning”. As its name suggests, this series aims to introduce you to some of the most frequently used interpretation (explanation) methods in deep learning. As a brief introduction, interpretation methods could help understand why a deep neural network predicts what it predicts and whether the high accuracy of the predictions of a model is meaningful.

Before proceeding any further, I highly recommend those of you who have not read the first article of this series to go through it (which is available here), as some of the methods covered here build upon the concepts introduced in the first article. In this post, I will cover another three important explanation methods, which include Layerwise Relevance Propagation (LRC), Local Interpretable Model-agnostic Explanations (LIME), and Input × Gradient.

Now let’s go through the details of these methods (as I already covered three of them in the previous article, the numbers here start from 4!):

## 4. Input × Gradient

## 5. Layerwise Relevance Propagation (LRC)

Layer-wise Relevance Propagation (LRC) aims to explain the predictions of a neural network by introducing a set of constraints and solving them. Any solutions to the constraints will be considered an acceptable explanation for the predictions of the network. In LRC, each dimension (*d*) of every layer (*l*) of the network has a relevance score (*R*) and the following equation should hold for the scores:

- Sum of relevance scores is constant across the different layers of the network: