![]() Local methods: Local methods explain the predictions of a model by investigating its performance on a specific set of examples.The following three categorizations are the most common ones found in the literature: The techniques covered in this article include Saliency maps, CAM, and Grad-CAM.īefore going into any details, it is helpful to get an overall picture of interpretation methods and how they work by trying to categorize them. As plenty of interpretation methods exist, I will cover only those that are applied to image classification neural networks in this post, and leave the other methods for separate posts. ![]() In this post, I will go through some of the most frequently used interpretation methods in deep learning, one of the fruitful areas of research in machine learning. To this end, many researchers have proposed several methods that help us gain insights into how these “black-box” models work. In order to build trust in machine learning models and move towards their integration into our everyday lives, we need to make “transparent” models that could explain why they predict what they predict. A Review of Different Interpretation Methods (Part 1: Saliency Map, CAM, Grad-CAM)
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