mlsanityData to 14 Oct 2026

TULiP: Test-time Uncertainty Estimation via Linearization and Weight Perturbation

arXiv 2505.16923 ↗First posted 22 May 2025stat.ML cs.LGcited by 2

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Abstract (arXiv)

A reliable uncertainty estimation method is the foundation of many modern out-of-distribution (OOD) detectors, which are critical for safe deployments of deep learning models in the open world. In this work, we propose TULiP, a theoretically-driven post-hoc uncertainty estimator for OOD detection. Our approach considers a hypothetical perturbation applied to the network before convergence. Based on linearized training dynamics, we bound the effect of such perturbation, resulting in an uncertainty score computable by perturbing model parameters. Ultimately, our approach computes uncertainty from a set of sampled predictions. We visualize our bound on synthetic regression and classification datasets. Furthermore, we demonstrate the effectiveness of TULiP using large-scale OOD detection benchmarks for image classification. Our method exhibits state-of-the-art performance, particularly for near-distribution samples.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 39 of its 57 references that are in the map. First posted on arXiv 22 May 2025.

Builds on

39 of 57 references.

References · 39

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27 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026DCAC: Dynamic Class-Aware Cache Creates Stronger Out-of-Distribution Detectorscited by 3
  2. 2026DynProto: Dynamic Prototype Evolution for Out-of-Distribution Detectioncited by 0