GENNI: Visualising the Geometry of Equivalences for Neural Network Identifiability

In this paper, we propose an efficient algorithm to visualise symmetries in neural networks. Typically the models are defined with respect to a parameter space, where non-equal parameters can produce the same function. Our proposed tool, GENNI, allows us to identify parameters that are functionally equivalent and to then visualise the subspace of the resulting equivalence class. Specifically, we experiment on simple cases, to demonstrate how to identify and provide possible solutions for more complicated scenarios.