Unsupervised Data Uncertainty Learning in Visual Retrieval Systems

Understand

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.