Phase Transition Frequency as a Training Time Predictor of Test Accuracy in ResNets

Abstract: The number of discrete class-separability jumps observed during ResNet finetuning is examined empirically as a predictor of final test accuracy. Across 75 experiments spanning four benchmarks (CIFAR-10, CIFAR-100, TinyImageNet, and CIFAR-10-C) and three architectures (ResNet-18, ResNet-50, and ResNet-101), with five to ten seeds per configuration, a strong within-dataset negative correlation is obtained on standard i.i.d. classification benchmarks: (r = -0.84) on CIFAR-10 ((p < 10^{-8}), (n = 30)) and (r = -0.87) on CIFAR-100 ((p < 10^{-5}), (n = 15)). Under distributional stress, the relationship attenuates: TinyImageNet yields (r = -0.45), and the CIFAR-10-C corruption benchmark yields (r = -0.19). Two additional analyses discipline the empirical claim. A partial correlation controlling for architecture depth, treated as a linear covariate, shows that on CIFAR-100 the transition count retains statistically significant predictive power ((r_{\mathrm{partial}} = -0.69), (p = 0.007)); the corresponding result under the stricter categorical conditioning is not established at (n = 15). A comparison against six alternative training-curve signals shows that transition count achieved the strongest correlation among the evaluated signals on CIFAR-100 and one of the strongest on CIFAR-10, but is dominated by other signals on the two stressed benchmarks. The comparison is restricted to training-curve-level signals; comparisons against effective rank, Hessian sharpness, Fisher information, margin, and neural-collapse measures, which are the strongest competitors in the current literature, are not part of the present study and remain open. The observation is presented as an in-distribution training-quality probe among a family of candidate probes, and an inexpensive detection procedure suitable for logging alongside a standard training loop is provided.