Real ResNet-18 feature spaces lose isotropy under continual learning, and forcing it back up hurts accuracy

Lanza, Pereira, Miozzo, Angelats & Dini measure IsoScore and a generalized IsoEntropy (covariance-eigenvalue-spectrum uniformity metrics) on penultimate-layer features of real ResNet-18 encoders trained via SupCon, Co2L, and two novel neural-collapse-inspired variants (SupCP, NCI) on CIFAR-10 and CIFAR-100 under three class-incremental continual-learning splits (50+50, 40+30+30, 20x5), comparing against non-continual centralized training [lanza-etal-2026-degradation-feature-space-continual-learning] IsoScore and IsoEntropy decline as more incremental experiences accumulate for most methods (SupCon accuracy drops from 94.98% centralized to 47.58% under the 20x5 CIFAR-10 split), but isotropy and accuracy are not consistently correlated across methods -- e.g. SupCP shows higher isotropy than NCI on CIFAR-100 while achieving lower accuracy [lanza-etal-2026-degradation-feature-space-continual-learning] Adding a differentiable isotropy regularizer to the Co2L loss causally raises IsoScore (from 0.023 to 0.969 at regularization strength 0.5 on CIFAR-10 20x5) but collapses accuracy from 70.64% to 14.74%, showing that forcibly restoring isotropy is actively harmful rather than protective in this continual-learning setting [lanza-etal-2026-degradation-feature-space-continual-learning]