Benchmarking Bayesian Deep Learning on Diabetic Retinopathy Detection Tasks. (arXiv:2211.12717v1 [stat.ML])

Bayesian deep learning seeks to equip deep neural networks with the ability
to precisely quantify their predictive uncertainty, and has promised to make
deep learning more reliable for safety-critical real-world applications. Yet,
existing Bayesian deep learning methods fall short of this promise; new methods
continue to be evaluated on unrealistic test beds that do not reflect the
complexities of downstream real-world tasks that would benefit most from
reliable uncertainty quantification. We propose the RETINA Benchmark, a set of
real-world tasks …

arxiv bayesian bayesian deep learning benchmarking deep learning detection