We propose two novel test-time fine-tuning methods to improve uncertain model predictions. Our methods require no auxiliary data and use the given test instance only. Instead of performing a greedy selection of the most likely class to make a prediction, we introduce an additional focus on the likely classes step during inference. By applying a single-step gradient descent, 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. Our theoretical discussion provides a deeper understanding highlighting the impact on shared and non-shared features among (focus) classes. The experimental evaluation highlights accuracy gains on samples exhibiting high decision uncertainty for a diverse set of models from both the text and image domain using the same hyperparameters.
View on arXiv@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 }
}