For the ground-truth label and N negative labels, we select K training examples for each label (K-shot learning, we set K=20).
if a label has less than K examples, we replicate the selected example to satisfy K. (N + 1) × K examples serve as a supporting set and the examples in S in l using a deep network (Any DL structure can be used for the encoder, such as LSTM and CNN.