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.

深度神经网络（DNN）是各种计算机视觉任务的强大工具，但它们经常在可靠的不确定性量化方面遇到困难 - 这是实际应用的关键要求。贝叶斯神经网络（BNN）能够进行不确定性估计，但无法扩展到高度不稳定的大型 DNNs，这些 DNNs 难以训练。为了解决这个挑战，我们引入了适应性贝叶斯神经网络（ABNN），这是一种简单且可扩展的策略，可以在后期以最小的计算和训练开销的方式将 DNNs 无缝转换为 BNNs。ABNN 通过简单的 BNN 适应层（附加到归一化层）和对预训练模型进行少量微调步骤，保留了 DNNs 的主要预测性能，并增强了它们的不确定性量化能力。我们在多个图像分类和语义分割任务的数据集上进行了大量实验证明，ABNN 在不需要集成方法通常需要的计算预算的情况下实现了最先进的性能。