Paper Overview
Field: Machine Learning Authors: Parampreet Singh, Anushka Singh, Sumit Kumar, Vipul Arora Published: 2026-08-22 arXiv: 2608.20326
Summary
Deep neural networks are often overconfident, assigning high confidence even to incorrect predictions. Post-hoc confidence estimation addresses this by training a lightweight auxiliary head on a frozen classifier, but existing objectives suffer from inherent ambiguity: the target values of correct and incorrect predictions overlap, making misclassified samples near the decision boundary difficult to distinguish from correctly classified ones.
This paper proposes TCPα, a novel confidence objective that addresses these limitations by introducing a margin-controlled penalty on misclassified samples. The authors theoretically prove that TCPα guarantees complete separation between the target values of correct and incorrect predictions, with a separation margin that is independent of the number of classes and increases monotonically with the penalty parameter.
Since an accurate classifier naturally produces very few errors, learning these targets results in a severely imbalanced regression problem. The paper systematically studies training strategies under this imbalance, identifying effective configurations through extensive ablation studies.
Results
In evaluations on raga recognition, rejecting only the lowest-confidence 8% of predictions improves the foundation model's macro-F1 from 0.89 to 0.98.
--- *Automatically collected on 2026-08-22*
Source: arXiv:2608.20326