What does "zero-shot learning" entail?

Zero-shot learning refers to the ability of a model to generalize to new tasks or categories without having been explicitly trained on examples of those tasks or categories. This approach leverages the existing knowledge the model has acquired from other related tasks to make predictions or perform tasks it has never encountered before.

When a model employs zero-shot learning, it can adapt to new situations by utilizing semantic information, such as the relationships between different categories or the characteristics that define them. This enables the model to perform actions such as classifying items, generating text, or identifying images based on descriptive attributes or contextual information, rather than requiring specific example data for every possible category.

The other choices focus on different aspects of model training and learning. For instance, learning from a limited amount of training data implies some prior exposure to the task at hand, which contrasts with the core principle of zero-shot learning. Training exclusively on labeled datasets focuses on supervised learning, which is fundamentally different as it requires having labeled examples for training. Lastly, improving model performance through supervised feedback refers to reinforcement learning or traditional supervised learning techniques, which again differ from the concept of zero-shot learning.