  We introduce an unsupervised formulation to estimate heteroscedastic
uncertainty in retrieval systems. We propose an extension to triplet loss that
models data uncertainty for each input. Besides improving performance, our
formulation models local noise in the embedding space. It quantifies input
uncertainty and thus enhances interpretability of the system. This helps
identify noisy observations in query and search databases. Evaluation on both
image and video retrieval applications highlight the utility of our approach.
We highlight our efficiency in modeling local noise using two real-world
datasets: Clothing1M and Honda Driving datasets. Qualitative results illustrate
our ability in identifying confusing scenarios in various domains. Uncertainty
learning also enables data cleaning by detecting noisy training labels.
