Does zero-shot learning have an error rate?

Yes, zero-shot learning inherently has an error rate. Like any machine learning model, it will not achieve perfect accuracy on unseen data or tasks.

Error rates in zero-shot learning typically stem from the fundamental challenge of recognizing entirely novel classes without prior training examples. Key factors include the quality and expressiveness of the auxiliary information (like semantic attributes or textual descriptions), the ability of the model to bridge the gap between seen and unseen classes, the inherent bias towards seen classes, and the risk of significant distribution shifts between training and inference data. The chosen architecture and the inherent difficulty of the unseen classes also heavily influence the error rate.

While generally yielding higher error rates compared to supervised learning on seen classes, the value of zero-shot learning lies in its ability to classify examples from classes absent during training. The practical goal is to minimize the error rate through improved model architectures, better auxiliary knowledge representations, and carefully designed evaluation protocols that reflect realistic deployment scenarios. Measuring this error rate is crucial for benchmarking progress.