Predictive Spectral Calibration for Source-Free Test-Time Regression

• URL: http://arxiv.org/abs/2603.09338v1
• Date: Tue, 10 Mar 2026 08:15:44 GMT
• Title: Predictive Spectral Calibration for Source-Free Test-Time Regression
• Abstract summary: Predictive Spectral (PSC) is a source-free framework that extends subspace alignment to block spectral matching.<n> PSC remains simple to implement, model-agnostic, and compatible with off-the-shelf pretrained regressors.
• Score: 0.764671395172401
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Test-time adaptation (TTA) for image regression has received far less attention than its classification counterpart. Methods designed for classification often depend on classification-specific objectives and decision boundaries, making them difficult to transfer directly to continuous regression targets. Recent progress revisits regression TTA through subspace alignment, showing that simple source-guided alignment can be both practical and effective. Building on this line of work, we propose Predictive Spectral Calibration (PSC), a source-free framework that extends subspace alignment to block spectral matching. Instead of relying on a fixed support subspace alone, PSC jointly aligns target features within the source predictive support and calibrates residual spectral slack in the orthogonal complement. PSC remains simple to implement, model-agnostic, and compatible with off-the-shelf pretrained regressors. Experiments on multiple image regression benchmarks show consistent improvements over strong baselines, with particularly clear gains under severe distribution shifts.

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