Bayesian Triplet Loss: Uncertainty Quantification in Image Retrieval

• URL: http://arxiv.org/abs/2011.12663v3
• Date: Fri, 17 Sep 2021 17:26:20 GMT
• Title: Bayesian Triplet Loss: Uncertainty Quantification in Image Retrieval
• Authors: Frederik Warburg, Martin J{\o}rgensen, Javier Civera, S{\o}ren Hauberg
• Abstract summary: Uncertainty quantification in image retrieval is crucial for downstream decisions. We present a new method that views image embeddings as features rather than deterministic features. We derive a variational approximation of the posterior, called the Bayesian triplet loss, that produces state-of-the-art uncertainty estimates.
• Score: 10.743633102172236
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Uncertainty quantification in image retrieval is crucial for downstream decisions, yet it remains a challenging and largely unexplored problem. Current methods for estimating uncertainties are poorly calibrated, computationally expensive, or based on heuristics. We present a new method that views image embeddings as stochastic features rather than deterministic features. Our two main contributions are (1) a likelihood that matches the triplet constraint and that evaluates the probability of an anchor being closer to a positive than a negative; and (2) a prior over the feature space that justifies the conventional l2 normalization. To ensure computational efficiency, we derive a variational approximation of the posterior, called the Bayesian triplet loss, that produces state-of-the-art uncertainty estimates and matches the predictive performance of current state-of-the-art methods.

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