Focus on the Likely: Test-time Instance-based Uncertainty Removal

We ask: Does focusing on classes predicted as likely improve model predictions? We aim for an affirmative answer by proposing two novel test-time fine-tuning methods to improve uncertain model predictions. Instead of greedily selecting the most likely class, we introduce an additional step, \emph{focus on the likely classes}, to refine predictions. By applying a theoretically motivated single gradient descent step with a large learning rate, we refine predictions when an initial forward pass indicates high uncertainty. This aligns predictions more closely with the ideal of assigning zero probability to less plausible outcomes. The experimental evaluation demonstrates accuracy gains for one of our methods, which emphasizes shared features among likely classes, across diverse text and image domain models. %Our theoretical discussion provides a deeper understanding, highlighting the varying impact of shared and non-shared features among (focus) classes. %Our discussion also suggests an interesting view on standard, offline training vs. test-time training: Opposing optimization rationales regarding breadth of feature dependence are preferable during each training phase.

@article{schneider2025_2505.03819,
  title={ Focus on the Likely: Test-time Instance-based Uncertainty Removal },
  author={ Johannes Schneider },
  journal={arXiv preprint arXiv:2505.03819},
  year={ 2025 }
}