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@ARTICLE{Paul:1033893,
      author       = {Paul, Richard D. and Quercia, Alessio and Fortuin, Vincent
                      and Nöh, Katharina and Scharr, Hanno},
      title        = {{P}arameter-efficient {B}ayesian {N}eural {N}etworks for
                      {U}ncertainty-aware {D}epth {E}stimation},
      publisher    = {arXiv},
      reportid     = {FZJ-2024-06731},
      year         = {2024},
      note         = {Presented as an Extended Abstract at the 3rd Workshop on
                      Uncertainty Quantification for Computer Vision at the
                      ECCV'24.},
      abstract     = {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.},
      keywords     = {Computer Vision and Pattern Recognition (cs.CV) (Other) /
                      Machine Learning (stat.ML) (Other) / FOS: Computer and
                      information sciences (Other)},
      cin          = {IAS-8 / IBG-1},
      cid          = {I:(DE-Juel1)IAS-8-20210421 / I:(DE-Juel1)IBG-1-20101118},
      pnm          = {5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs)
                      and Research Groups (POF4-511)},
      pid          = {G:(DE-HGF)POF4-5112},
      typ          = {PUB:(DE-HGF)25},
      doi          = {10.48550/ARXIV.2409.17085},
      url          = {https://juser.fz-juelich.de/record/1033893},
}