Spectral Surgery: Class-Targeted Post-Hoc Rebalancing via Hessian Spike Perturbation

ArXi:2605.07790v1 Announce Type: new The Hessian spectrum of trained deep networks exhibits a characteristic structure: a continuous bulk of near-zero eigenvalues and a small number of large outlier eigenvalues (spikes), confirming the relevance of Random Matrix Theory in deep learning. The spike count matches the number of classes minus one. While prior work has described this structure, no method has exploited it operationally to improve classification performance.