TCAV Introduction

Understanding deep learning models remains an open challenge in machine learning research. The concept of “understanding” itself is subjective and varies significantly depending on one’s technical background and perspective. This complexity is frequently addressed in research papers, where different approaches to model interpretability yield different insights.

What makes a model truly interpretable? Several excellent blog posts have explored this question:

• How neural networks build up their understanding of images
• The building blocks of interpretability

While these resources offer valuable perspectives on model interpretation, TCAV (Testing with Concept Activation Vectors) takes a unique approach. It builds on the observation that specific neural network layers become activated by distinct features or “concepts” more than others.