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cs.CV2023
Laplacian Segmentation Networks Improve Epistemic Uncertainty Quantification
Kilian Zepf, Selma Wanna, Marco Miani +5
Image segmentation relies heavily on neural networks which are known to be overconfident, especially when making predictions on out-of-distribution (OOD) images. This is a common s…
cs.LG2023
Bayesian Metric Learning for Uncertainty Quantification in Image Retrieval
Frederik Warburg, Marco Miani, Silas Brack +1
We propose the first Bayesian encoder for metric learning. Rather than relying on neural amortization as done in prior works, we learn a distribution over the network weights with…