Deep neural networks are highly effective when a large number of labeled
samples are available but fail with few-shot classification tasks. Recently,
meta-learning methods have received much attention, which train a meta-learner
on massive additional tasks to gain the knowledge to instruct the few-shot
classification. Usually, the training tasks are randomly sampled and performed
indiscriminately, often making the meta-learner stuck into a bad local optimum.
Some works in the optimization of deep neural networks have shown that a better
arrangement of training data can make the classifier converge faster and
perform better. Inspired by this idea, we propose an easy-to-hard expert
meta-training strategy to arrange the training tasks properly, where easy tasks
are preferred in the first phase, then, hard tasks are emphasized in the second
phase. A task hardness aware module is designed and integrated into the
training procedure to estimate the hardness of a task based on the
distinguishability of its categories. In addition, we explore multiple hardness
measurements including the semantic relation, the pairwise Euclidean distance,
the Hausdorff distance, and the Hilbert-Schmidt independence criterion.
Experimental results on the miniImageNet and tieredImageNetSketch datasets show
that the meta-learners can obtain better results with our expert training
strategy.

该研究提出了一种易 - 难专家元训练策略和任务难度感知模块，用于优化元学习方法，使其在少样本分类任务上表现更好。该策略优先进行易任务的训练，再进行难任务的强化。实验结果显示，该方法在 miniImageNet 和 tieredImageNetSketch 数据集上可以获得更好的结果。