XAI — Build Your Own Deep-learning interpretation Algorithm

XAI — Build Your Own Deep-learning interpretation Algorithm

You have probably heard about LIME, SHAP, or Grad-CAM libraries and how they can help you spot areas of interest used by deep learning models for computer vision or object detection… but did you ever thought about making your own? This article will help you create it

You have probably heard about LIME, SHAP, or Grad-CAM libraries and how they can help you spot areas of interest used by deep learning models for computer vision or object detection… but did you ever thought about making your own? Here is a step-by-step tutorial!

A few weeks ago, I started working on a new project: using cameras and deep-learning to evaluate the level of product in a silo. By monitoring this level, we could run specific operations only when needed, instead of a predefined frequency: 

Example of an industrial computer vision system (here product level detection)

Image by Author

Showing the first results to my boss who has a pretty good understanding of Artificial Intelligence, the comment he made was so simple and so right:

“What is fascinating is that the model provides accurate predictions… but we ignore how!”

Of course, as human beings, we tend to believe that the model does focus on the product itself to determine its level but numerous experiences show that models sometimes monitor different (and usually as relevant!) areas to optimize their predictions!

The need for Explainable A.I. (XAI) led to the existence of packages such as SHAP or LIME which provide ways of reducing the complexity behind deep-learning models and understanding how predictions are made:

tensorflow deep-learning explainable-ai towards-data-science computer-vision

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