Learn how to use Explainable AI  to design and implement popular explanation algorithms.

Mastering Image Classification

Explainable AI is a set of tools and frameworks that helps us understand and interpret the internal logic behind the predictions made by a deep learning network. With this, we can generate insights into the behavior and working of a model to mitigate issues around it in the development phase. 


In this module, we will be introduced to popular Explainable AI algorithms such as smooth gradient, integrated gradient, LIME, class activation maps, counterfactual explanations, feature attributions, etc., for image classification networks such as MobileNet-V2 trained on large-scale datasets like ImageNet-1K.

By the end of this module, we will understand the need for Explainable AI and be able to design and implement popular explanation algorithms like saliency maps, class activation maps, counterfactual explanations, etc. We will be able to evaluate and quantify the quality of the neural network explanations via several interpretability metrics.

Interpreting Image Classifier Decisions

Learn about an enhanced version of GradCAM that satisfies two explainability axioms—sensitivity and conservation.

X-GradCAM

Axiom-based GradCAM