Supplementary Material: Bayesian Metric Learning for Uncertainty Quantification in Image Retrieval

The target, or label, is the value that encodes the information we want to learn. Instead, we express the loss in an equivalent but more verbose way. In the previous Section, we defined the contrastive loss for the entire dataset (14). This intuition is formalized by the following Definition and Proposition. Then the loss, as defined in Eq. 14, can be approximated by using the target in Eq. 20 with L (; D) |D The equality is proven by applying the logic of Eq. 19 two times independently, once for the We highlight that this scaling is linear, and thus is reflected in both first and second-order derivatives.