Invertible Logits Transformation for Accuracy-Preserving Post-Hoc Uncertainty Calibration

• URL: http://arxiv.org/abs/2608.10372v1
• Date: Tue, 11 Aug 2026 02:08:05 GMT
• Title: Invertible Logits Transformation for Accuracy-Preserving Post-Hoc Uncertainty Calibration
• Abstract summary: An ideal calibrator should correct nonlinear miscalibration, scale gracefully to large label spaces, and preserve the original predictions.<n>InvLT consistently outperforms a broad set of post-hoc baselines on standard calibration metrics.
• Score: 10.333478793903724
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Post-hoc calibration aligns a classifier's predicted confidences with its empirical accuracy without retraining. An ideal calibrator should correct nonlinear miscalibration, scale gracefully to large label spaces, and preserve the original predictions; existing methods typically violate at least one of these properties---temperature scaling lacks expressivity, more flexible parametric alternatives introduce parameters that grow with the number of classes $C$, and other expressive methods do not preserve the rank ordering of class scores and may alter the predicted class. We propose \textbf{Invertible Logits Transformation (InvLT)}, which applies a learned scalar MLP $f:\mathbb{R}\to\mathbb{R}$ element-wise to the pre-softmax logits. Sharing $f$ across all logit dimensions makes the parameter count independent of $C$. Monotonicity of $f$---and hence preservation of the argmax prediction---is softly encouraged via a paired inverse network rather than enforced through the numerical integration required by prior monotone calibrators; this avoids their computational overhead while empirically preserving the original classification accuracy in every setting we evaluate. Across standard image classification benchmarks and a range of architectures, InvLT consistently outperforms a broad set of post-hoc baselines on standard calibration metrics.

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