Deploying deep learning models in safety-critical applications remains a very challenging task, mandating the provision of assurances for the dependable operation of these models. Uncertainty quantification (UQ) methods estimate the model's confidence per prediction, informing decision-making by considering the effect of randomness and model misspecification. Despite the advances of state-of-the-art UQ methods, they are computationally expensive or produce conservative prediction sets/intervals. We introduce MC-CP, a novel hybrid UQ method that combines a new adaptive Monte Carlo (MC) dropout method with conformal prediction (CP). MC-CP adaptively modulates the traditional MC dropout at runtime to save memory and computation resources, enabling predictions to be consumed by CP, yielding robust prediction sets/intervals. Throughout comprehensive experiments, we show that MC-CP delivers significant improvements over advanced UQ methods, like MC dropout, RAPS and CQR, both in classification and regression benchmarks. MC-CP can be easily added to existing models, making its deployment simple.

翻译：在安全关键应用中部署深度学习模型仍然是一项极具挑战性的任务，要求对这些模型的可靠运行提供保证。不确定性量化方法通过评估模型每次预测的置信度，考虑随机性和模型设定偏差的影响来辅助决策。尽管先进的不确定性量化方法取得了进展，但它们要么计算成本高昂，要么产生保守的预测集/区间。我们提出了MC-CP，这是一种新颖的混合不确定性量化方法，将新型自适应蒙特卡洛丢弃法与共形预测相结合。MC-CP在运行时自适应地调节传统蒙特卡洛丢弃法，以节省内存和计算资源，使预测结果能够被共形预测处理，从而生成鲁棒的预测集/区间。通过全面的实验，我们展示了MC-CP在分类和回归基准测试中均优于先进的不确定性量化方法（如MC丢弃法、RAPS和CQR）。MC-CP可以轻松添加到现有模型中，使其部署变得简单。