Embeddings are numerical representations of objects, such as words, sentences, or images, that capture their semantic meaning in a continuous vector space. In the context of zero-shot learning, embeddings allow models to make predictions about classes or tasks they have not seen during training. By placing different classes in a shared embedding space, the model can leverage relationships between known and unknown categories, enabling it to infer information about unfamiliar classes based on their proximity to known ones.
For example, consider a model that has been trained to recognize various animal classes, such as dogs, cats, and birds. If the model encounters a new class, like "horses," during a zero-shot learning scenario, embeddings can help. If the embedding for "horses" is located near that of "dogs" and "cats" in the vector space, the model can make educated guesses about the characteristics of horses based on these similar embeddings. This allows the model to classify new data effectively without needing to have been explicitly trained on the new class.
Moreover, embeddings facilitate knowledge transfer across different tasks or domains. For instance, when dealing with text, embeddings like those from Word2Vec, GloVe, or BERT can help a model understand that "car" and "automobile" are synonymous, even if it has never encountered the word "automobile" during training. This capacity to generalize enables developers to create more adaptive models that can function in diverse environments with limited data, significantly enhancing their practical usability across various applications.