Parameter-efficient Bayesian Neural Networks for Uncertainty-aware Depth Estimation
<|reference_start|>Parameter-efficient Bayesian Neural Networks for Uncertainty-aware Depth Estimation: State-of-the-art computer vision tasks, like monocular depth estimation (MDE), rely heavily on large, modern Transformer-based architectures. However, their application in safety-critical domains demands reliable predictive performance and uncertainty quantification. While Bayesian neural networks provide a conceptually simple approach to serve those requirements, they suffer from the high dimensionality of the parameter space. Parameter-efficient fine-tuning (PEFT) methods, in particular low-rank adaptations (LoRA), have emerged as a popular strategy for adapting large-scale models to down-stream tasks by performing parameter inference on lower-dimensional subspaces. In this work, we investigate the suitability of PEFT methods for subspace Bayesian inference in large-scale Transformer-based vision models. We show that, indeed, combining BitFit, DiffFit, LoRA, and CoLoRA, a novel LoRA-inspired PEFT method, with Bayesian inference enables more robust and reliable predictive performance in MDE.<|reference_end|>
@article{paul2024parameter-efficient,
    title={Parameter-efficient Bayesian Neural Networks for Uncertainty-aware Depth
  Estimation},
    author={Richard D. Paul, Alessio Quercia, Vincent Fortuin, Katharina N"oh,
  Hanno Scharr},
    journal={arXiv preprint arXiv:2409.17085},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.17085},
    primaryClass={cs.CV stat.ML}
}