Deep Neural Networks (DNNs) are powerful tools for various computer vision tasks, yet they often struggle with reliable uncertainty quantification - a critical requirement for real-world applications. Bayesian Neural Networks (BNN) are equipped for uncertainty estimation but cannot scale to large DNNs that are highly unstable to train. To address this challenge, we introduce the Adaptable Bayesian Neural Network (ABNN), a simple and scalable strategy to seamlessly transform DNNs into BNNs in a post-hoc manner with minimal computational and training overheads. ABNN preserves the main predictive properties of DNNs while enhancing their uncertainty quantification abilities through simple BNN adaptation layers (attached to normalization layers) and a few fine-tuning steps on pre-trained models. We conduct extensive experiments across multiple datasets for image classification and semantic segmentation tasks, and our results demonstrate that ABNN achieves state-of-the-art performance without the computational budget typically associated with ensemble methods.

翻译：深度神经网络（DNN）是各种计算机视觉任务的有力工具，但它们在可靠的不确定性量化方面常常表现不佳——这是实际应用中的关键要求。贝叶斯神经网络（BNN）具备不确定性估计能力，但无法扩展到高度不稳定训练的大型DNN。为应对这一挑战，我们引入了自适应贝叶斯神经网络（ABNN），这是一种简单且可扩展的策略，能够以极小的计算和训练开销，以后验方式将DNN无缝转化为BNN。ABNN保留了DNN的主要预测性能，同时通过简单的BNN适配层（附加在归一化层上）以及对预训练模型的少量微调步骤，增强了其不确定性量化能力。我们在多个数据集上针对图像分类和语义分割任务进行了广泛实验，结果表明，ABNN无需集成方法通常所需的计算预算即可实现最先进的性能。