Geometric compression of invariant manifolds in neural nets

Jonas Paccolat, Leonardo Petrini, Mario Geiger +2

We study how neural networks compress uninformative input space in models where data lie in $d$ dimensions, but whose label only vary within a linear manifold of dimension $d_\para…