Unsupervised Latent Space Alignment with Hyperspherical Geodesic Matching
Researchers propose a method for aligning neural network latent spaces without requiring shared sample correspondences.
The paper introduces Hyperspherical Geodesic Matching to align independently trained latent spaces. By leveraging the geometric properties of latent representations, the approach avoids the need for anchor-based supervision, addressing a common bottleneck in unsupervised model alignment.