IG can be applied to any differentiable model.
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How will your model generalize to new fireboats? What about fireboats without water jets? Read on to learn more about how IG works and how to apply IG to your models to better understand the relationship between their predictions and underlying features.ĭownload a pretrained image classifier from TF-Hub In the images below titled "IG Attribution Mask" and "Original + IG Mask Overlay" you can see that your model instead highlights (in purple) the pixels comprising the boat's water cannons and jets of water as being more important than the boat itself to its decision. Your model will also classify this image as a fireboat later on in this tutorial however, does it highlight the same pixels as important when explaining its decision? You would classify this image as a fireboat and might highlight the pixels making up the boat and water cannons as being important to your decision. As an example, consider this image of a fireboat spraying jets of water.
In this tutorial, you will walk through an implementation of IG step-by-step to understand the pixel feature importances of an image classifier. images, text, structured data), ease of implementation, theoretical justifications, and computational efficiency relative to alternative approaches that allows it to scale to large networks and feature spaces such as images. IG has become a popular interpretability technique due to its broad applicability to any differentiable model (e.g. It has many use cases including understanding feature importances, identifying data skew, and debugging model performance. IG aims to explain the relationship between a model's predictions in terms of its features.
This tutorial demonstrates how to implement Integrated Gradients (IG), an Explainable AI technique introduced in the paper Axiomatic Attribution for Deep Networks.
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