LENS: Learning and Exploitation of Latent Space Geometries

LENS brings together researchers studying the geometry of latent representations their manifolds, Riemannian structures, intrinsic dimensions, and implications for model design and evaluation. We aim to bridge advances in geometric learning with practical computer vision applications, fostering dialogue between the theory and deployments.

We welcome contributions that deepen our understanding of latent spaces (e.g., curvature, geodesics, topology), propose geometry-aware architectures and objectives, or demonstrate how latent geometry can improve robustness, generalization, fairness, privacy, and efficiency in real-world vision systems.