  Existing meta-learning based few-shot learning (FSL) methods typically adopt
an episodic training strategy whereby each episode contains a meta-task. Across
episodes, these tasks are sampled randomly and their relationships are ignored.
In this paper, we argue that the inter-meta-task relationships should be
exploited and those tasks are sampled strategically to assist in meta-learning.
Specifically, we consider the relationships defined over two types of meta-task
pairs and propose different strategies to exploit them. (1) Two meta-tasks with
disjoint sets of classes: this pair is interesting because it is reminiscent of
the relationship between the source seen classes and target unseen classes,
featured with domain gap caused by class differences. A novel learning
objective termed meta-domain adaptation (MDA) is proposed to make the
meta-learned model more robust to the domain gap. (2) Two meta-tasks with
identical sets of classes: this pair is useful because it can be employed to
learn models that are robust against poorly sampled few-shots. To that end, a
novel meta-knowledge distillation (MKD) objective is formulated. There are some
mistakes in the experiments. We thus choose to withdraw this paper.
