Certifiably Robust Interpretation in Deep Learning

Understand

Deep learning interpretation is essential to explain the reasoning behind model predictions.

• Understanding the robustness of interpretation methods is important especially in sensitive domains such as medical applications since interpretation results are often used in downstream tasks.
• Although gradient-based saliency maps are popular methods for deep learning interpretation, recent works show that they can be vulnerable to adversarial attacks.
• In this paper, we address this problem and provide a certifiable defense method for deep learning interpretation.