DAFE disentangles hidden states into different parts so that the network can learn representations for particular domains or tasks, as illustrated in Figure 1.
it consists of three parts: a base network with parameters θ base that learns common features across different tasks and domains, a domain-aware feature embedding learner that generates embeddings θ τ domain given input domain τ and a task-aware feature embedding learner that outputs task representations θ γ task given input task γ.