- A relation network consists of two important functions: the embedding function, denoted by
, and the relation function, denoted by 
. - Once we have the feature vectors of the support set,
, and query set, 
, we combine them using an operator, 
. Here, 
can be any combination operator; we use concatenation as an operator to combine the feature vectors of the support set and the query set—that is, 
.
- The relation function,
, will generate a relation score ranging from 0 to 1, representing the similarity between samples in the support set, 
, and samples in the query set, 
. -
Our loss function can be represented as follows:
- In matching networks, we use two embedding functions,
and 
, to learn the embeddings of the query set 
and the support set 
, respectively. -
The output,
, for the query point, 
, can be predicted as follows: