Representational Similarity via Interpretable Visual Concepts
Neehar Kondapaneni Oisin Mac Aodha Pietro Perona
How do two deep neural networks differ in how they arrive at a decision? Measuring  the similarity of deep networks has been a long-standing open question. Most existing methods provide a single number to measure the similarity of two networks at a given layer, but give no insight into what makes them similar or dissimilar. We introduce an interpretable representational similarity method (RSVC) to compare two networks. We use RSVC to discover shared and unique visual concepts between two models. We show that some aspects of model differences can be attributed to unique concepts discovered by one model that are not well represented in the other. Finally, we conduct extensive evaluation across different vision model architectures and training protocols to demonstrate its effectiveness.
representational alignment, representational similarity, explainability, interpretability, comparison
a method to visually, interpretably compare two neural networks.