In the last chapter, we learned about prototypical networks and how variants of prototypical networks, such as Gaussian prototypical and semi-prototypical networks, are used for one-shot learning. We have seen how prototypical networks make use of embeddings to perform classification tasks.

In this chapter, we will learn about relation networks and matching networks. First, we will see what a relation network is and how it is used in one-shot, few-shot, and zero-shot learning settings, after which we will learn how to build a relation network using TensorFlow. Later in this chapter, we will learn about matching networks and how they are used in few-shot learning. We will also see different types of embedding functions used in matching networks. At the end of this chapter, we will see how to build matching networks in Tensorflow.

In this chapter, we will learn about the following:

• Relation networks
• Relation networks in one-shot, few-shot, and zero-shot settings
• Building relation networks using TensorFlow
• Matching networks
• The embedding functions of a matching network
• The architecture of matching networks
• Matching networks in TensorFlow