Can zero-shot learning reduce annotation costs?

Yes, zero-shot learning (ZSL) can significantly reduce annotation costs. It enables models to recognize new classes without requiring labeled training data specific to those classes.

ZSL reduces costs primarily by leveraging existing knowledge and semantic relationships (e.g., attributes, descriptions) learned from base classes to infer unseen classes. Key considerations include the necessity of a pre-existing knowledge base linking seen and unseen classes and the quality of the model's initial training. Performance often hinges on the accuracy of the semantic embeddings and the robustness of the knowledge transfer, potentially trading off some accuracy compared to fully supervised methods.

Its core value lies in efficiently scaling models to handle new categories and domains without expensive labeling efforts. It is particularly valuable in dynamic applications like image recognition (identifying rare species), NLP (new slang/entities), and recommendation systems, where continuously collecting new labeled data is impractical or cost-prohibitive.