Make Me a BNN: A Simple Strategy for Estimating Bayesian Uncertainty
  from Pre-trained Models
Gianni Franchi Olivier Laurent Maxence Leguéry Andrei Bursuc Andrea Pilzer Angela Yao
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.