Figure 4:

Architecture for transfer learning: in the context of few-shot learning, transfer learning involves using a base task to train the base classifier (f()). In this example, the base classifier is trained on the task of addiction/recovery detection (text classification). The learned embeddings from the base classifier are used to produce embeddings with data-level prior knowledge. The embeddings are used to train the target classifier (g()) on a different, but related text classification task: illicit drug detection.