Zero-shot learning (ZSL) models leverage semantic knowledge by using pre-existing information about classes or concepts to make predictions on unseen categories. This approach bypasses the need for extensive training data for every possible class. Instead, ZSL models typically rely on attributes or descriptive features that represent classes. For instance, rather than only training a model on images of cats and dogs, it can learn from words or attributes like "has fur" or "four-legged," which apply to a broader range of animals. When the model encounters a new class, like a horse, it can leverage its understanding of the attributes associated with it to make predictions, even without specific training examples.
The semantic knowledge can come from various sources, such as word embeddings, ontologies, or relationship graphs. For example, if a model is trained with word embeddings that capture similarities between words, it may find that "horse" is semantically close to "zebra" because of shared attributes like "striped" or "herbivore." This allows the model to infer characteristics of the unseen class based on its similarity to known classes. In essence, the model can use these relationships to navigate the space of possible classes without needing direct examples of every class.
Furthermore, the use of semantic knowledge also allows for flexibility and adaptability in ZSL applications. For instance, in an image classification scenario, developers can introduce new categories simply by providing the relevant attributes rather than collecting and labeling data for each new category. This can significantly reduce the time and resources needed for data preparation. Additionally, it encourages a broader understanding of how different classes relate to one another, which can lead to more robust and generalizable models in various applications, such as natural language processing and image recognition.