Now, we will see another interesting one-shot learning algorithm, called a relation network. It is one of the simplest and most efficient one-shot learning algorithms. We will explore how relation networks are used in one-shot, few-shot, and zero-shot learning settings.
A relation network consists of two important functions: the embedding function, denoted by
, and the relation function, denoted by
. The embedding function is used for extracting the features from the input. If our input is an image, then we can use a convolutional network as our embedding function, which will give us the feature vectors/embeddings of an image. If our input is a text, then we can use LSTM networks to get the embeddings of the text.
As we know, in one-shot learning, we have only a single example per class. For example, let's say our support set contains three classes with one example per class. As shown in the following diagram, we have a support set containing...
