Integrated Gradients Explained — Theory, Axioms & Python Implementation

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Integrated Gradients is one of the most theoretically grounded methods for explaining deep learning computer vision models. It works by averaging gradients along a path of interpolated images from a baseline to the original input, then multiplying by the pixel-wise difference. In this lesson, we break down the mathematics behind the method, compare it to DeepLIFT, discuss the axioms it satisfies, and explain how to interpret the resulting saliency maps as marginal contributions rather than absolute importance. We then apply the method in Python using Captum, exploring how the number of interpolation steps affects the attribution. This lesson is part of a free explainable AI course for computer vision and deep learning.

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🚀 Chapters 🚀
00:00 Introduction
01:31 The maths
04:00 The interpretation
06:32 Comparison to DeepLIFT
07:34 Axioms
09:05 Application with Python
13:41 Varying the steps

Видео Integrated Gradients Explained — Theory, Axioms & Python Implementation канала A Data Odyssey