Spatial Adapter: Structured Spatial Decomposition and Closed-Form Covariance for Frozen Predictors
We present the Spatial Adapter, a parameter-efficient post-hoc layer that equips any frozen first-stage predictor with a structured spatial representation of its residual field and an induced closed-form spatial covariance. The adapter operates as a cascade second stage on residuals, jointly learning a spatially regularized orthonormal basis and per-sample scores via a tractable mini-batch ADMM procedure, without modifying any first-stage parameter. Because the first-stage parameters are frozen, the adapter does not retrain the backbone; its role is to supply a compressed distributional summary of the residual field. Smoothness, sparsity, and orthogonality together turn a generic low-rank...
| Authors | Wen-Ting Wang, Wei-Ying Wu, Hao-Yun Huang et al. |
|---|---|
| Primary category | stat.ML |
| Published | 12 May 2026 |
| Last updated at source | 12 May 2026 |
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