1-Lipschitz Neural Networks on Hadamard Manifolds
This work constructs and analyzes a class of 1-Lipschitz neural networks on Hadamard manifolds, and shows improved results from the nonexpansive denoiser over static, data-only, and Log-Euclidean denoising baselines, and empirically test its convergence properties.