Prospective Prediction of OOD Degradation from Source-Side Training Dynamics

Abstract:We study whether persistent out-of-distribution (OOD) degradation can be predicted before it is directly observed using only source-side training dynamics. In a controlled shortcut-learning setting, a simple logistic regression predictor develops a clear prospective signal, while training time alone does not. Temporal summaries of the source-side quantities are substantially more informative than their current values. When transferred without additional training from a CNN to an MLP, confidence and entropy dynamics retain substantial predictive information. These results provide a proof of principle that source-side training dynamics can contain an early warning signal for future OOD failure.

Machine Learning (cs.LG)

arXiv:2610.12397 [cs.LG]

(or arXiv:2610.12397v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.12397 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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