Model Interpretability and Understanding for PyTorch using Captum

Introduction

Methods of model interpretability have gained growing significance in recent years as a direct consequence of the rise in model complexity and the associated lack of transparency. Model understanding is a hot topic of study and a focal area for practical applications employing machine learning across various sectors.

Captum supplies academics and developers with cutting-edge techniques, such as Integrated Gradients, that make it simple to identify the elements that contribute to a model’s output. Captum makes it easier for ML researchers to use PyTorch models to build interpretability methods.

By making it easier to identify the many elements that contribute to a model’s output, Captum can help model developers create better models and fix models that provide unexpected results.