Interpreting CNN Knowledge via an Explanatory Graph

Understand

This paper learns a graphical model, namely an explanatory graph, which reveals the knowledge hierarchy hidden inside a pre-trained CNN.

• Considering that each filter in a conv-layer of a pre-trained CNN usually represents a mixture of object parts, we propose a simple yet efficient method to automatically disentangles different part patterns from each filter, and construct an explanatory graph.
• In the explanatory graph, each node represents a part pattern, and each edge encodes co-activation relationships and spatial relationships between patterns.
• More importantly, we learn the explanatory graph for a pre-trained CNN in an unsupervised manner, i.e., without a need of annotating object parts.