Bayesian Metric Learning for Uncertainty Quantification in Image Retrieval

• URL: http://arxiv.org/abs/2302.01332v2
• Date: Sat, 4 Feb 2023 14:11:00 GMT
• Title: Bayesian Metric Learning for Uncertainty Quantification in Image Retrieval
• Authors: Frederik Warburg, Marco Miani, Silas Brack, Soren Hauberg
• Abstract summary: We propose the first Bayesian encoder for metric learning. We learn a distribution over the network weights with the Laplace Approximation. 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.
• Score: 0.7646713951724012
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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