DATS: Distance-Aware Temperature Scaling for Calibrated Class-Incremental Learning

• URL: http://arxiv.org/abs/2509.21161v1
• Date: Thu, 25 Sep 2025 13:46:56 GMT
• Title: DATS: Distance-Aware Temperature Scaling for Calibrated Class-Incremental Learning
• Authors: Giuseppe Serra, Florian Buettner,
• Abstract summary: Continual Learning (CL) is gaining increasing attention for its ability to enable a single model to learn incrementally from a sequence of new classes.<n>In safety-critical applications, predictive models should also be able to reliably communicate their uncertainty in a manner - that is, with confidence scores aligned to the true frequencies of target events.<n>We propose Distance-Aware Temperature Scaling (DATS), which combines prototype-based distance estimation with distance-aware calibration to infer task proximity and assign adaptive temperatures without prior task information.
• Score: 13.864609787260298
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Continual Learning (CL) is recently gaining increasing attention for its ability to enable a single model to learn incrementally from a sequence of new classes. In this scenario, it is important to keep consistent predictive performance across all the classes and prevent the so-called Catastrophic Forgetting (CF). However, in safety-critical applications, predictive performance alone is insufficient. Predictive models should also be able to reliably communicate their uncertainty in a calibrated manner - that is, with confidence scores aligned to the true frequencies of target events. Existing approaches in CL address calibration primarily from a data-centric perspective, relying on a single temperature shared across all tasks. Such solutions overlook task-specific differences, leading to large fluctuations in calibration error across tasks. For this reason, we argue that a more principled approach should adapt the temperature according to the distance to the current task. However, the unavailability of the task information at test time/during deployment poses a major challenge to achieve the intended objective. For this, we propose Distance-Aware Temperature Scaling (DATS), which combines prototype-based distance estimation with distance-aware calibration to infer task proximity and assign adaptive temperatures without prior task information. Through extensive empirical evaluation on both standard benchmarks and real-world, imbalanced datasets taken from the biomedical domain, our approach demonstrates to be stable, reliable and consistent in reducing calibration error across tasks compared to state-of-the-art approaches.

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