<ul><li>Covariance matrices within the Symmetric Positive Definite (SPD) manifold play a crucial role in various scientific fields.</li><li>Researchers have developed neural networks on the SPD manifold to accurately learn covariance embeddings.</li><li>The existing Riemannian batch normalization (RBN) approach may not effectively handle ill-conditioned SPD matrices.</li><li>A novel Riemannian batch normalization (RBN) algorithm based on the Generalized Bures-Wasserstein metric (GBWM) is proposed for improved performance.</li></ul>

Learning to Normalize on the SPD Manifold under Bures-Wasserstein Geometry

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