TopoGeoScore: A Self-Supervised Source-Only Geometric Framework for OOD Checkpoint Selection

• URL: http://arxiv.org/abs/2605.08870v1
• Date: Sat, 09 May 2026 10:46:47 GMT
• Title: TopoGeoScore: A Self-Supervised Source-Only Geometric Framework for OOD Checkpoint Selection
• Abstract summary: We propose textbfTopoGeoScore, a source-only geometric scorer for label-free OOD checkpoint selection.<n>We construct class-conditional mutual $k$-nearest-neighbour graphs from source embeddings and extract three interpretable signals.<n>Instead of fixing their weights by hand, TopoGeoScore learns a non-negative linear score through a self-supervised objective.
• Score: 1.4877856019517555
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Out-of-distribution (OOD) robustness is difficult to diagnose when target-domain labels are unavailable. We consider a more restrictive source-only variant of unsupervised accuracy estimation: selecting robust checkpoints using only source-domain representations, with no target samples or target labels. We propose \textbf{TopoGeoScore}, a source-only geometric scorer for label-free OOD checkpoint selection. Given a trained checkpoint, we construct class-conditional mutual $k$-nearest-neighbour graphs from source embeddings and extract three interpretable signals: a torsion-inspired reduced Laplacian log-determinant for global class-manifold complexity, Ollivier--Ricci curvature for local neighbourhood regularity, and higher-order topological summaries for fragmented connectivity, loops, and global--local inconsistency. Instead of fixing their weights by hand, TopoGeoScore learns a non-negative linear score through a self-supervised objective that enforces invariance under approximately geometry-preserving embedding views and separation from structure-breaking views. The score remains interpretable and uses no target-domain samples or labels. Results across CIFAR-based corruption and distribution-shift benchmarks, ImageNet-C, MNLI$\to$HANS transfer, and OGBN-Arxiv suggest that source representations contain measurable global--local--topological evidence of robustness, supporting practical checkpoint selection before deployment under distribution shift.

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