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 1 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. Each graph node represents a part pattern, and graph edges encode co-activation relationships and spatial relationships between patterns. We learn the explanatory graph for a pre-trained CNN in an unsupervised manner, i.e. without a need of annotating object parts. Experiments show that each graph node consistently represents the same object part among different images. We transfer part patterns in the explanatory graph to part localization, and our method greatly outperforms other approaches.
Zhang et al. REF ) transformed CNN representations to an explanatory graph, which represents the semantic hierarchy hidden inside a pre-trained CNN.
Interpreting CNN knowledge via an Explanatory Graph
ArXiv
ArXiv
Computer Science