This post will help you to understand the two basic axioms of Integrated Gradients and how to implement Integrated Gradient using TensorFlow using a transfer learned model.

What is Integrated Gradient?

Integrated Gradient(IG) is an interpretability or explainability technique for deep neural networks which visualizes its input feature importance that contributes to the model’s prediction

Can IG be applied to only a specific use case of deep learning or only to a specific neural network architecture?

Integrated Gradient(IG) computes the gradient of the model’s prediction output to its input featuresand requires no modification to the original deep neural network.

IG can be applied to any differentiable model like image, text, or structured data.

IG can be used for

  • Understanding feature importance by extracting rules from the network
  • Debugging deep learning models performance
  • Identifying data skew by understanding the important features contributing to the prediction

#integrated-gradient #deep-learning #python #ai-explainability #tensorflow

Understanding Deep Learning Models with Integrated Gradients
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