For safety-related applications, it is crucial to produce trustworthy deep neural networks whose prediction is associated with confidence that can represent the likelihood of correctness for subsequent decision-making. Existing dense binary classification models are prone to being over-confident. To improve model calibration, we propose Adaptive Stochastic Label Perturbation (ASLP) which learns a unique label perturbation level for each training image. ASLP employs our proposed Self-Calibrating Binary Cross Entropy (SC-BCE) loss, which unifies label perturbation processes including stochastic approaches (like DisturbLabel), and label smoothing, to correct calibration while maintaining classification rates. ASLP follows Maximum Entropy Inference of classic statistical mechanics to maximise prediction entropy with respect to missing information. It performs this while: (1) preserving classification accuracy on known data as a conservative solution, or (2) specifically improves model calibration degree by minimising the gap between the prediction accuracy and expected confidence of the target training label. Extensive results demonstrate that ASLP can significantly improve calibration degrees of dense binary classification models on both in-distribution and out-of-distribution data. The code is available on https://github.com/Carlisle-Liu/ASLP.

翻译：针对安全相关应用，生成可信的深度神经网络至关重要，这类网络的预测需关联置信度，以反映后续决策中预测正确的可能性。现有密集二分类模型易出现过自信问题。为改进模型校准，我们提出自适应随机标签扰动（ASLP），该方法为每张训练图像学习独特的标签扰动水平。ASLP 采用我们提出的自校准二元交叉熵（SC-BCE）损失函数，该函数统一了多种标签扰动过程（包括随机方法如DisturbLabel和标签平滑），在保持分类性能的同时校正校准误差。ASLP 遵循经典统计力学的最大熵推断原理，在信息缺失条件下最大化预测熵，并在此过程中实现以下目标之一：(1) 作为保守解，保持已知数据的分类精度；(2) 通过最小化预测精度与目标训练标签预期置信度之间的差距，专门提升模型校准程度。大量实验结果表明，ASLP 能显著改善密集二分类模型在分布内数据与分布外数据上的校准度。代码已开源：https://github.com/Carlisle-Liu/ASLP。