  To accelerate learning process with few samples, meta-learning resorts to
prior knowledge from previous tasks. However, the inconsistent task
distribution and heterogeneity is hard to be handled through a global sharing
model initialization. In this paper, based on gradient-based meta-learning, we
propose an ensemble embedded meta-learning algorithm (EEML) that explicitly
utilizes multi-model-ensemble to organize prior knowledge into diverse specific
experts. We rely on a task embedding cluster mechanism to deliver diverse tasks
to matching experts in training process and instruct how experts collaborate in
test phase. As a result, the multi experts can focus on their own area of
expertise and cooperate in upcoming task to solve the task heterogeneity. The
experimental results show that the proposed method outperforms recent
state-of-the-arts easily in few-shot learning problem, which validates the
importance of differentiation and cooperation.
