Hands-On Meta Learning with Python

Summary

In this chapter, we started off with prototypical networks, and we saw how a prototypical network computes the class prototype using the embedding function and predicts the class label of the query set by comparing the Euclidean distance between the class prototype and query set embeddings. Following this, we experimented with a prototypical network by performing classification on an omniglot dataset. Then, we learned about the Gaussian prototypical network, which, along with the embeddings, also uses the covariance matrix to compute the class prototype. Following this, we explored semi-prototypical networks, which are used to handle semi-supervised classes. In the next chapter, we will learn about relation and matching networks. ...