Embeddings handle rare or unseen data by mapping such data points to locations in the embedding space that are close to similar, seen data points. For example, if a rare word or image is encountered, it can be represented by finding the closest match among the existing embeddings in the model. This is particularly useful in cases like zero-shot learning, where the model needs to make predictions for classes or data it has never encountered before.
In some cases, embeddings for rare or unseen data may not be as accurate as for more common data, especially if the model has not been trained with enough diversity. However, models like those trained with unsupervised or self-supervised learning can generalize well to new data by learning broad patterns and relationships. Techniques like transfer learning, where embeddings from a pre-trained model are fine-tuned on a specific task, can also improve performance on unseen data.
While embeddings are generally good at handling unseen data, they may still struggle in situations where there is a lack of relevant context or sufficient training data. As such, the ability of embeddings to generalize depends on the diversity and quality of the data used to train the models, as well as the specific task at hand.