Hub for Outliers, Spokes for Inliers: Uniform Latent Space Construction for Dual-Mismatched Semi-Supervised Learning
The method addresses dual-mismatched semi-supervised learning by creating a hub-spoke latent geometry. Known classes are uniformly distributed around a central hub, while high-uncertainty unknown samples are guided toward this hub. This structure organizes features to improve class discriminability and separate uncertain samples from known ones, leading to better performance than existing methods.