Leveraging Bayesian deep learning and ensemble methods for uncertainty quantification in image classification: A ranking-based approach.

ABSTRACT: Bayesian deep learning (BDL) has emerged as a powerful technique for quantifying uncertainty in classification tasks, surpassing the effectiveness of traditional models by aligning with the probabilistic nature of real-world data. This alignment allows for informed decision-making by not only identifying the most likely outcome but also quantifying the surrounding uncertainty. Such capabilities hold great significance in fields like medical diagnoses and autonomous driving, where the consequences of misclassification are substantial. To further improve uncertainty quantification, the research community has introduced Bayesian model ensembles, which combines multiple Bayesian models to enhance predictive accuracy and uncertainty quantification. These ensembles have exhibited superior perfor

SUBMITTER: Abdullah AA 

PROVIDER: S-EPMC10825337 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature