Visual Concept Connectome (VCC): Open World Concept Discovery and Their Interlayer Connections in Deep Models
Matthew D. Kowal, Richard P. Wildes, Konstantinos G. Derpanis · 2024
Understanding what deep network models capture in their learned representations is a fundamental challenge in computer vision. We present a new methodology to understanding such vision models, the Visual Concept Con-nectome (VCC), which discovers human interpretable concepts and their interlayer connections in a fully unsuper-vised manner. Our approach simultaneously reveals fine-grained concepts at a layer, connection weightings across all layers and is amendable to global analysis of network structure (e.g. branching pattern of hierarchical concept assemblies). Previous work yielded ways to extract inter-pretable concepts from single layers and examine their im-pact on classification, but did not afford multilayer concept analysis across an entire network architecture. Quantitative and qualitative empirical results show the effectiveness of VCCs in the domain of image classification. Also, we lever-age VCCs for the application of failure mode debugging to reveal where mistakes arise in deep networks.