Adaptive-Depth Layer Grafting: Representation-Level Transfer Across Heterogeneous Tabular Feature Spaces

Transfer learning is especially challenging when a label-scarce target comes from a disparate domain and shares the same prediction endpoint as a source model but uses a different feature space, with neither paired samples nor known feature correspondence. We ask whether knowledge encoded in a pretrained source hierarchy can still be leveraged under this schema mismatch. To that effect, we introduce Adaptive-Depth Layer Grafting (ADLG), which maps target inputs through a lightweight bridge into a selected internal source representation while keeping the retained source tail fixed. ADLG-Frozen trains only the bridge and the interface selection is handled separately, with quantile-aligned CKA used as one optional pre-fit screening method. On Census-Income→Adult, ADLG improves balanced accuracy over a model trained from scratch by 1.52, 2.56, and 2.89 percentage points with 0.5%, 2%, and 10% of the target labels, respectively, and by 2.36 points across the prespecified 0.5–20% label regime. It also gains 1.88 points over a tuned target-only MLP and 3.41 points in minority recall over the model trained from scratch. Additional studies span heterogeneous tabular settings, including structured multimodal and inter-modal representations, as well as multiclass and regression endpoints. Controlled audits show that attachment depth is consequential and that source-label information is most discernible at shallow interfaces. Overall, ADLG provides a compact mechanism for leveraging knowledge in a pretrained source hierarchy when schemas differ and target labels are scarce.