A prototype network in few-shot learning is a type of neural network architecture designed to enable a model to recognize new classes with only a small number of training examples. Instead of learning a separate classifier for each class, prototype networks learn to generate a single representative point, or "prototype," for each class based on a limited number of training samples. During the training process, the model computes these prototypes in a feature space where the distance between prototypes reflects the similarity between classes. When presented with a new input, the model compares the input's features to each prototype and assigns it to the closest one, allowing it to make predictions even with minimal data.
The architecture typically involves two main components: a feature extractor and a distance metric. The feature extractor processes input images or data to create embeddings that capture the essential characteristics of the input. The distance metric, often Euclidean distance, calculates the similarity between these embeddings and the prototypes. For instance, in an image classification task where you have only a few examples of a new animal species, the prototype network creates an embedding for each existing species and then develops a prototype for the new species based on its limited examples. When a new image is presented, the network determines which prototype it is closest to and classifies it accordingly.
Prototype networks excel in scenarios where collecting ample training data is impractical. An example application is in medical imaging, where labeling new diseases might be expensive or time-consuming. With prototype networks, a model can effectively identify new conditions based on only a few annotated scans. Overall, prototype networks facilitate efficient learning and adaptability in fields where data scarcity poses a significant challenge.