Zero-shot learning finds application in scenarios where the availability of labeled training data for all possible classes or tasks is limited or impractical. For instance, consider the task of sentiment analysis for customer reviews in a rapidly evolving industry, where new product categories or features emerge frequently. In such cases, training a sentiment analysis model on labeled data for every new product category or feature may be impractical or resource-intensive. Instead, zero-shot learning allows the model to generalize from existing labeled data for related product categories and adapt to new categories without the need for additional labeled examples. By leveraging shared semantic similarities and transfer learning, zero-shot learning enables the model to make accurate predictions for unseen or novel classes, thereby enhancing its scalability and adaptability in dynamic environments.
