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AI Features

Integrated Gradients

Explore how integrated gradients overcome the vanishing gradient problem to generate precise saliency maps for image classifiers. This lesson guides you through the implementation steps, explains the importance of baseline interpolation, and demonstrates how integrated gradients produce clearer and more robust explanations than vanilla gradients, aiding interpretation and model analysis.

Integrated gradients

Deep neural networks are often trained to the saturation. Because of this, their predictions are often flat in the proximity of the input, like the following:

If a neural network f(.)f(.) is flat near the proximity of the input XX, its gradient with respect to XXis given by X f(X)0\nabla_X \ f(X) \approx 0 ...