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NeurIPS | 2022
Tight Lower Bounds on Worst-Case Guarantees for Zero-Shot Learning with Attributes
Abstract
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.