Now, we will look at a variant of a prototypical network, called a Gaussian prototypical network. We just learned how a prototypical network learns the embeddings of the data points and how it builds the class prototype by taking the mean embeddings of each class and uses the class prototype for performing classification.
In a Gaussian prototypical network, along with generating embeddings for the data points, we add a confidence region around them, characterized by a Gaussian covariance matrix. Having a confidence region helps in characterizing the quality of individual data points and would be useful in the case of noisy and less homogeneous data.
So, in Gaussian prototypical networks, the output of the encoder will be embeddings, as well as the covariance matrix. Instead of using the full covariance matrix, we either include a radius or diagonal component from the covariance matrix along with the embeddings: