Curriculum-Based Meta-learning

Meta-learning offers an effective solution to learn new concepts with scarce supervision through an episodic training scheme: a series of target-like tasks sampled from base classes are sequentially fed into a meta-learner to extract common knowledge across tasks, which can facilitate the quick acquisition of task-specific knowledge of the target task with few samples. Despite its noticeable improvements, the episodic training strategy samples tasks randomly and uniformly, without considering their hardness and quality, which may not progressively improve the meta-leaner's generalization ability. In this paper, we present a Curriculum-Based Meta-learning (CubMeta) method to train the meta-learner using tasks from easy to hard. Specifically, the framework of CubMeta is in a progressive way, and in each step, we design a module named BrotherNet to establish harder tasks and an effective learning scheme for obtaining an ensemble of stronger meta-learners. In this way, the metalearner's generalization ability can be progressively improved, and better performance can be obtained even with fewer training tasks. We evaluate our method for few-shot classification on two benchmarks - mini-ImageNet and tiered-ImageNet, where it achieves consistent performance improvements on various meta-learning paradigms.