Deep neural networks, despite their high accuracy, often exhibit poor confidence calibration, limiting their reliability in high-stakes applications. Current ad-hoc confidence calibration methods attempt to fix this during training but face a fundamental trade-off: two-phase training methods achieve strong classification performance at the cost of training instability and poorer confidence calibration, while single-loss methods are stable but underperform in classification. This paper addresses and mitigates this stability-performance trade-off. We propose Socrates Loss, a novel, unified loss function that explicitly leverages uncertainty by incorporating an auxiliary unknown class, whose predictions directly influence the loss function and a dynamic uncertainty penalty. This unified objective allows the model to be optimized for both classification and confidence calibration simultaneously, without the instability of complex, scheduled losses. We provide theoretical guarantees that our method regularizes the model to prevent miscalibration and overfitting. Across four benchmark datasets and multiple architectures, our comprehensive experiments demonstrate that Socrates Loss consistently improves training stability while achieving more favorable accuracy-calibration trade-off, often converging faster than existing methods.

翻译：深度神经网络虽具有高精度，但在高风险应用中常因置信度校准不良而限制其可靠性。当前针对性的置信度校准方法试图在训练阶段进行修正，却面临根本性权衡：两阶段训练方法虽能实现优秀分类性能，但以训练不稳定和校准效果恶化为代价；而单损失方法虽训练稳定，分类表现却不尽如人意。本文旨在解决并缓解这种稳定性与性能之间的权衡问题。我们提出苏格拉底损失（Socrates Loss），一种创新的统一损失函数——通过引入辅助未知类显式利用不确定性，使模型预测直接影响损失函数并施加动态不确定性惩罚项。这种统一优化目标使模型能够同时优化分类与置信度校准，避免了复杂调度损失带来的不稳定性。理论证明表明，该方法可正则化模型以防止校准偏差与过拟合。在四个基准数据集及多种架构上的综合实验显示，苏格拉底损失能持续提升训练稳定性，同时实现更优的精度-校准权衡，且收敛速度普遍优于现有方法。