What is the primary challenge addressed by zero-shot learning?

Zero-shot learning primarily addresses the challenge of making predictions in scenarios involving classes or categories that the model has not previously encountered during training. This technique enables models to generalize knowledge from known classes to effectively identify and classify unseen classes based on semantic information, such as descriptions or attributes associated with those classes.

In typical supervised learning, models rely heavily on labeled data for training; however, acquiring labeled data for every possible class can be impractical or impossible. Zero-shot learning provides a powerful solution by allowing the model to leverage its understanding of related concepts and transfer knowledge, ultimately broadening its capabilities beyond the limitations of its training dataset. This is particularly beneficial in dynamic environments where new classes frequently emerge, and it is not feasible to retrain models with new data each time.

In contrasting choices, insufficient training data would suggest a broader range of challenges, while overfitting relates to the model’s performance being restricted to known classes rather than extending into new ones. Lack of performance evaluation metrics, although important in assessment, does not directly pertain to the core concept of zero-shot learning which is the ability to predict on unseen classes.