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| Hauptseite > Publikationsdatenbank > Parameter-efficient Bayesian Neural Networks for Uncertainty-aware Depth Estimation > print |
| Hauptseite > Publikationsdatenbank > Parameter-efficient Bayesian Neural Networks for Uncertainty-aware Depth Estimation > print |
| 001 | 1033893 | ||
| 005 | 20241217215531.0 | ||
| 024 | 7 | _ | |a 10.48550/ARXIV.2409.17085 |2 doi |
| 024 | 7 | _ | |a 10.34734/FZJ-2024-06731 |2 datacite_doi |
| 037 | _ | _ | |a FZJ-2024-06731 |
| 100 | 1 | _ | |a Paul, Richard D. |0 P:(DE-Juel1)175101 |b 0 |e Corresponding author |u fzj |
| 245 | _ | _ | |a Parameter-efficient Bayesian Neural Networks for Uncertainty-aware Depth Estimation |
| 260 | _ | _ | |c 2024 |b arXiv |
| 336 | 7 | _ | |a Preprint |b preprint |m preprint |0 PUB:(DE-HGF)25 |s 1734418575_31226 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a WORKING_PAPER |2 ORCID |
| 336 | 7 | _ | |a Electronic Article |0 28 |2 EndNote |
| 336 | 7 | _ | |a preprint |2 DRIVER |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a Output Types/Working Paper |2 DataCite |
| 500 | _ | _ | |a Presented as an Extended Abstract at the 3rd Workshop on Uncertainty Quantification for Computer Vision at the ECCV'24. |
| 520 | _ | _ | |a 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. |
| 536 | _ | _ | |a 5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511) |0 G:(DE-HGF)POF4-5112 |c POF4-511 |f POF IV |x 0 |
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| 650 | _ | 7 | |a Computer Vision and Pattern Recognition (cs.CV) |2 Other |
| 650 | _ | 7 | |a Machine Learning (stat.ML) |2 Other |
| 650 | _ | 7 | |a FOS: Computer and information sciences |2 Other |
| 700 | 1 | _ | |a Quercia, Alessio |0 P:(DE-Juel1)188471 |b 1 |u fzj |
| 700 | 1 | _ | |a Fortuin, Vincent |0 P:(DE-HGF)0 |b 2 |
| 700 | 1 | _ | |a Nöh, Katharina |0 P:(DE-Juel1)129051 |b 3 |u fzj |
| 700 | 1 | _ | |a Scharr, Hanno |0 P:(DE-Juel1)129394 |b 4 |u fzj |
| 773 | _ | _ | |a 10.48550/ARXIV.2409.17085 |
| 856 | 4 | _ | |u https://arxiv.org/abs/2409.17085 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/1033893/files/2409.17085v1.pdf |y OpenAccess |
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| 914 | 1 | _ | |y 2024 |
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| 980 | _ | _ | |a preprint |
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| 980 | 1 | _ | |a FullTexts |
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