Zero-shot learning (ZSL) is a technique that allows models to make predictions on classes or tasks for which they have not seen any training data. Instead of relying solely on examples during training, ZSL leverages additional information, often in the form of attributes or semantic descriptions, to facilitate understanding of new classes. This way, even if a model has not encountered specific examples, it can still classify or generate outputs based on learned relationships and knowledge about the existing categories.
One common method used in zero-shot learning involves the use of embeddings or representations for both the seen and unseen classes. For instance, if a model has been trained on animal images and knows about dogs and cats, it can also learn attributes like "has stripes" or "can fly." When presented with a new class, such as a zebra, the model can use the learned attribute "has stripes" to infer that a zebra might belong to a new category based on its appearance, even if it has never seen a zebra before. This ability to connect attributes or descriptions allows the model to generalize knowledge beyond the specific instances it has encountered during training.
Furthermore, zero-shot learning is particularly useful in scenarios where collecting labeled data is impractical, such as rare species identification or niche product classification. In such cases, developers can provide rich semantic descriptions or metadata that relate existing categories to new ones. For example, in image recognition, by describing the features of a "red panda" using attributes like "small," "has a bushy tail," or "found in trees," the model can recognize and classify it within its knowledge framework. Overall, zero-shot learning enables models to adapt and perform in diverse, real-world scenarios without needing extensive labeled datasets for every possible class.