Tight Lower Bounds on Worst-Case Guarantees for Zero-Shot Learning with Attributes

This paper demonstrates a mathematical analysis of zero-shot learning with attributes, providing a tight lower bound on the worst-case error of the best map from attributes to classes and showing that this bound is predictive of how standard zero-shot methods behave in practice.