Meta-Learning across Meta-Tasks for Few-Shot Learning
This addresses few-shot learning for AI systems needing rapid adaptation with limited data, but it is incremental as it builds on existing meta-learning frameworks.
The paper tackled the problem of few-shot learning by proposing to exploit relationships between meta-tasks during meta-learning, introducing meta-domain adaptation and meta-knowledge distillation objectives to improve robustness to domain gaps and poorly sampled few-shots. However, the paper was withdrawn due to mistakes in the experiments, so no concrete results are reported.
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.