feature-specialization

The paper introduces Persistence Forcing (PerF), which assigns heterogeneous refinement budgets to feature groups in pixel-space Diffusion Transformers, producing persistent global-structure features that guide active detail features. PerF-H reaches FID 1.63 on ImageNet 256x256, outperforming JiT-H with competitive efficiency.

A theoretical study shows that in overparameterized ReLU networks, a positive-homogeneous scaling gauge hidden in the initial parameters can deterministically control which duplicate neuron learns a teacher feature, affecting specialization time and pruning trajectories.