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 lightweight auxiliary heads on frozen classifiers, but existing objectives suffer from inherent ambiguity: target values for correct and incorrect predictions overlap, making it hard to distinguish errors near the decision boundary from correct predictions.
This paper proposes TCPα, a novel confidence target that resolves these limitations by introducing a margin-controlled penalty for 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 accurate classifiers naturally produce very few errors, learning these targets leads to a severely imbalanced regression problem. The paper systematically studies training strategies under this imbalance and identifies effective configurations through extensive ablation studies.
Evaluation on raga recognition shows that rejecting only the 8% lowest-confidence predictions improves a foundation model's macro-F1 from 0.89 to 0.98.
Link: arXiv:2608.20326
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