Covariance Last-Layer Ensembles: Function-Space Diversity for Efficient Uncertainty Quantification
A Last-Layer Ensemble (LLE), $K$ linear units on one shared frozen feature map, is an efficient single-pass approach to the disagreement-based epistemic uncertainty for out-of-distribution (OOD) detection. Its weakness is that members share the backbone gradient and can converge toward the same function, collapsing the inter-member diversity the signal depends on. Whether last-layer diversity can be restored, and what mitigates the collapse, is an open question. The weight-orthonormality defining Orthonormal Certificates (OC), the weight-orthonormal special case of the LLE, is only an indirect correction; it decorrelates the weights of the members, not their predictions. Here, we instead ...
| Authors | H. Martin Gillis, Isaac Xu, Gabriel Spadon et al. |
|---|---|
| Primary category | cs.LG |
| Published | 26 July 2026 |
| Last updated at source | 28 July 2026 |
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