Enhancing Image Classification with Bayesian Neural Networks: Improved Training Techniques and Decision Strategies for Reliable AI Support in Uncertainty-Sensitive Tasks

Bayesian theory is a probabilistic statistical framework for dealing with uncertainty and incomplete information, while neural networks are widely applied in deep learning fields such as image processing. Bayesian Neural Networks (BNNs) combine Bayesian theory and neural networks to provide more comprehensive predictive results. Although BNNs have significant advantages in expressing model uncertainty, their training methods and network structure design are still to be explored. Effectively training BNNs and exploring their network structure design to enhance predictive performance and computational efficiency are the core of this research. The study aims to improve the performance of BNNs in image classification tasks, specifically including the quantification and utilization of uncertainty to enhance model reliability and decision support capabilities. We use efficient training methods such as variational inference to redesign Bayesian neural networks and conduct deep image recognition training on the CIFAR-10 dataset. The improved training method allows for the rapid and accurate estimation of model weight uncertainty, while the proposed decision strategy utilizes uncertainty information to make reliable decisions. This study demonstrates that BNNs have tremendous potential in addressing real-world uncertainty issues, with the results expected to provide safer AI decision support for risk-sensitive tasks and promote the reliability and practicality of deep learning technology in various critical applications.