Figure 2:

Architecture for matching networks: a small support set contains some instances with their labels (one instance per label in the figure). Given a query, the goal is to calculate a value that indicates if the instance is an example of a given class. For a similarity metric, two embedding functions, f() and g(), need to take similarity based on the feature space. The function f(), which is a neural network, is applied first, and then the embedding function g() is applied to each instance to process the kernel for each support set. (Note: example uses the DASH 2020 Drug Data [18]).