A common architecture used in few-shot learning is the Siamese Network. This architecture consists of two identical subnetworks that share the same weights and parameters. The main idea behind the Siamese Network is to learn how to differentiate between pairs of inputs by comparing their feature representations. It processes two inputs and outputs a similarity score, which helps determine if the inputs belong to the same class. This is particularly useful in few-shot learning scenarios, where the model must generalize from only a few examples of each class.
In practical terms, the Siamese Network takes a pair of images and calculates their embeddings using a neural network, such as a convolutional neural network (CNN). The embeddings represent the images in a lower-dimensional space. A contrastive loss function is typically employed at this stage, which encourages the network to minimize the distance between the embeddings of similar pairs while maximizing the distance for dissimilar pairs. This way, the model learns a robust feature representation that can be applied to new, unseen classes with very few examples to facilitate recognition.
Another example of a few-shot learning architecture is the Prototypical Network. Instead of comparing pairs, this architecture derives a prototype for each class based on the available examples. During training, Prototypical Networks calculate the mean embedding of each class from its samples. At inference, new samples are assigned to the class whose prototype is closest to their embedding. This method can be very effective because it uses the statistical properties of the small dataset to make decisions, aligning well with the goals of few-shot learning by maximizing learning efficiency even with limited data. Together, Siamese Networks and Prototypical Networks provide powerful approaches for developers working on tasks that involve few-shot learning.