Hands-On Meta Learning with Python

Algorithm

Now, we will better understand the Gaussian prototypical network by going through it step by step:

1. Let's say we have a dataset, D = {(x₁, y₁,), (x₂, y₂), ... (x_i, y_i)}, where x is the feature and y is the label. Let's say we have a binary label, which means we have only two classes, 0 and 1. We will sample data points at random without replacement from each of the classes from our dataset, D, and create our support set, S.
2. Similarly, we sample data points at random per class and create the query set, Q.
3. We will pass the support set to our embedding function, f(). The embedding function will generate the embeddings for our support set, along with the covariance matrix.
4. We calculate the inverse of the covariance matrix.
5. We compute ...