The Dynamic Net Architecture: Learning Robust and Holistic Visual Representations Through Self-Organizing Networks

Pascal Sager, Jan Milan Deriu, Benjamin F. Grewe, Thilo Stadelmann, Christoph von der Malsburg

Abstract: Nets, cooperative networks of neurons, have been proposed as format for the representation of sensory signals, as physical implementation of the Gestalt phenomenon and as solution to the neural binding problem, while the direct interaction between nets by structure-sensitive matching has been proposed as basis for object-global operations such as object detection. The nets are flexibly composed of overlapping net fragments, which are learned from statistical regularities of sensory input. We here present the cooperative network architecture (CNA), a concrete model that learns such net structure to represent input patterns and deals robustly with noise, deformation, and out-of-distribution data, thus laying the groundwork for a novel neural architecture.