arXiv Analytics

Sign in

arXiv:2302.01332 [cs.LG]AbstractReferencesReviewsResources

Bayesian Metric Learning for Uncertainty Quantification in Image Retrieval

Frederik Warburg, Marco Miani, Silas Brack, Soren Hauberg

Published 2023-02-02Version 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 the Laplace Approximation. We actualize this by first proving that the contrastive loss is a valid log-posterior. We then propose three methods that ensure a positive definite Hessian. Lastly, we present a novel decomposition of the Generalized Gauss-Newton approximation. Empirically, we show that our Laplacian Metric Learner (LAM) estimates well-calibrated uncertainties, reliably detects out-of-distribution examples, and yields state-of-the-art predictive performance.

Comments: Code:
Categories: cs.LG, cs.CV
Related articles: Most relevant | Search more
arXiv:2306.14430 [cs.LG] (Published 2023-06-26)
Enhanced multi-fidelity modelling for digital twin and uncertainty quantification
arXiv:2302.04019 [cs.LG] (Published 2023-02-08)
Fortuna: A Library for Uncertainty Quantification in Deep Learning
arXiv:2211.14545 [cs.LG] (Published 2022-11-26)
Ensemble Multi-Quantile: Adaptively Flexible Distribution Prediction for Uncertainty Quantification