{
  "id": "2302.01332",
  "version": "v1",
  "published": "2023-02-02T18:59:23.000Z",
  "updated": "2023-02-02T18:59:23.000Z",
  "title": "Bayesian Metric Learning for Uncertainty Quantification in Image Retrieval",
  "authors": [
    "Frederik Warburg",
    "Marco Miani",
    "Silas Brack",
    "Soren Hauberg"
  ],
  "comment": "Code: https://github.com/FrederikWarburg/bayesian-metric-learning",
  "categories": [
    "cs.LG",
    "cs.CV"
  ],
  "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.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2023-02-02T18:59:23.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "bayesian metric learning",
      "uncertainty quantification",
      "image retrieval",
      "first bayesian encoder",
      "laplacian metric learner"
    ],
    "tags": [
      "github project"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}