用优化算法提升深度学习不确定性估计的可靠性
Enhancing Monte Carlo Dropout Performance for Uncertainty Quantification
- 引入灰狼、粒子群等优化算法改进蒙特卡洛丢弃方法
- 在多个数据集上准确率与不确定性评估均提升2-3%
- 适合医疗诊断等高风险场景中的模型可信度提升
掌握深度神经网络输出的不确定性对做出可信决策至关重要，尤其在医学诊断和自动驾驶等高风险领域。蒙特卡洛丢弃（MCD）是一种广泛应用的不确定性量化方法，可轻松集成到各类深度架构中。然而，传统MCD常难以提供校准良好的不确定性估计。为此，本文提出新框架，通过引入灰狼优化器（GWO）、贝叶斯优化（BO）、粒子群优化（PSO）以及不确定性感知损失函数，增强MCD性能。我们在DenseNet121、ResNet50、VGG16等骨干网络上，于猫狗分类、心肌炎、威斯康星州及合成环形数据集（Circles）上进行全面实验。所提方法在准确率和不确定性准确性上平均优于基准MCD 2-3%，且校准效果显著更优。结果表明该方法可有效提升深度学习模型在安全关键应用中的可信度。
原文摘要 · Abstract (English)
Knowing the uncertainty associated with the output of a deep neural network is of paramount importance in making trustworthy decisions, particularly in high-stakes fields like medical diagnosis and autonomous systems. Monte Carlo Dropout (MCD) is a widely used method for uncertainty quantification, as it can be easily integrated into various deep architectures. However, conventional MCD often struggles with providing well-calibrated uncertainty estimates. To address this, we introduce innovative frameworks that enhances MCD by integrating different search solutions namely Grey Wolf Optimizer (GWO), Bayesian Optimization (BO), and Particle Swarm Optimization (PSO) as well as an uncertainty-aware loss function, thereby improving the reliability of uncertainty quantification. We conduct comprehensive experiments using different backbones, namely DenseNet121, ResNet50, and VGG16, on various datasets, including Cats vs. Dogs, Myocarditis, Wisconsin, and a synthetic dataset (Circles). Our proposed algorithm outperforms the MCD baseline by 2-3% on average in terms of both conventional accuracy and uncertainty accuracy while achieving significantly better calibration. These results highlight the potential of our approach to enhance the trustworthiness of deep learning models in safety-critical applications.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。