Embeddings play a crucial role in few-shot and zero-shot learning by providing a way to represent complex data in a lower-dimensional space, where similarities between items can be easily measured. Essentially, embeddings are vectors that capture the semantics or important features of data points, enabling models to understand and categorize new information even with limited examples. In few-shot learning, where only a handful of labeled examples are available for a task, embeddings allow the model to generalize from these few samples by leveraging similarities across different classes. For example, if a model has been trained on various animal categories, it can learn to classify a new animal by comparing its embedding to those of known animals.
In zero-shot learning, embeddings facilitate the process of making predictions for classes that the model has never seen during training. This is often achieved through the alignment of textual descriptions or attributes with visual data representations. For instance, a model may have embeddings for animals based on their visual features and associate them with textual descriptions like "has stripes" or "is large." When asked to classify a new animal, say a zebra, the model can use the textual embedding of its description to find similarities with previously learned embeddings, thus making predictions without direct examples. This method allows for a flexible transfer of knowledge across different classes.
To maximize the effectiveness of embeddings in these scenarios, developers often use pre-trained models that already capture rich representations of data, like those from convolutional neural networks (CNNs) for images or transformers for text. By leveraging these embeddings, developers can create applications capable of performing complex tasks with minimal training data. An example could be in a medical imaging application where only a few examples of a rare condition are available. The model can utilize learned embeddings from a broader dataset of common conditions to make informed classifications about the rare condition based on its similarities to previously seen data. This approach not only enhances the performance of the model but also reduces the need for extensive labeled datasets.