Efficient Bayesian Neural Networks for Uncertainty-aware Depth Estimation in Computer Vision

Combining parameter-efficient fine-tuning methods like LoRA, CoLoRA, BitFit, and DiffFit with Bayesian inference techniques like SWAG and checkpoint ensembles enables robust and reliable predictive performance in large-scale Transformer-based monocular depth estimation models.