SPHERE-JEPA: Family of Statistical Regularizers for Hypersphere

SPHERE-JEPA introduces deterministic statistical regularizers on the hypersphere, replacing stochastic sliced methods with analytically integrated objectives like MMD, KSD, and KL divergence. Rotationally invariant kernels based on heat and bandlimited filters ensure spatial bias-free learning, with empirical results showing improved convergence and performance on ImageNet and Galaxy10, and superior instance separation in procedural texture retrieval using KL divergence.