Matching networks are yet another simple and efficient one-shot learning algorithm published by Google's DeepMind team. It can even produce labels for the unobserved class in the dataset.
Let's say we have a support set, S, containing K examples as 
. When given a query point (a new unseen example), 
, the matching network predicts the class of 
by comparing it with the support set.
We can define this as 
, where 
is the parameterized neural network, 
is the predicted class for the query point, 
, and 
is the support set. will return the probability of 
belonging to each of the classes in the dataset. Then, we select the class of 
as the one that has the highest probability. But how does this work exactly? How is this probability computed? Let's us see that now.
The output, 
, for the query point, 
, can be predicted as follows:
Let's decipher...
