arXiv AI
A Controlled Evaluation of Curvature Inspired Scalar Feature Scaling for Domain Generalization
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A Controlled Evaluation of Curvature Inspired Scalar Feature Scaling for Domain Generalization
The paper evaluates a narrow implementation of domain‑specific curvature called HyperDG, which rescales Euclidean encoder features during training with learned positive scalars and uses a standard linear classifier at inference. Across six conditions on four benchmark datasets (PACS, VLCS, Office Home, TerraIncognita) and 384 runs, the method’s macro performance is 1.61 percentage points below native DomainBed ERM, with no statistically significant difference after adjustment. The study concludes that this scalar‑scaling approach does not provide evidence of improvement over ERM and offers a bounded negative result for this specific training package.
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