标题： 无剧集任务选择用于少样本学习 显示英文标题

标题： Episodic-free Task Selection for Few-shot Learning

摘要： 基于情节的训练是小样本学习的一种主流训练策略。 然而，在小样本场景中，这种策略通常不如一些非情节的训练策略，例如邻域组件分析（NCA），这挑战了训练条件必须与测试条件匹配的原则。 因此，一个自然的问题被提出：如何搜索无情节的任务以实现更好的小样本学习？ 在本工作中，我们提出了一种超越情节训练的新型元训练框架。 在这个框架中，情节任务不直接用于训练，而是用于评估从任务集中选择的一些无情节任务的有效性，这些任务用于训练元学习器。 选择标准是通过亲和力设计的，亲和力衡量在使用选定任务进行训练后执行目标任务时损失减少的程度。 在实验中，训练任务集包含一些有前景的类型，例如对比学习和分类，目标小样本任务是在miniImageNet、tiered-ImageNet和CIFAR-FS数据集上使用最近中心分类器实现的。 实验结果证明了我们方法的有效性。 显示英文摘要

摘要： Episodic training is a mainstream training strategy for few-shot learning. In few-shot scenarios, however, this strategy is often inferior to some non-episodic training strategy, e. g., Neighbourhood Component Analysis (NCA), which challenges the principle that training conditions must match testing conditions. Thus, a question is naturally asked: How to search for episodic-free tasks for better few-shot learning? In this work, we propose a novel meta-training framework beyond episodic training. In this framework, episodic tasks are not used directly for training, but for evaluating the effectiveness of some selected episodic-free tasks from a task set that are performed for training the meta-learners. The selection criterion is designed with the affinity, which measures the degree to which loss decreases when executing the target tasks after training with the selected tasks. In experiments, the training task set contains some promising types, e. g., contrastive learning and classification, and the target few-shot tasks are achieved with the nearest centroid classifiers on the miniImageNet, tiered-ImageNet and CIFAR-FS datasets. The experimental results demonstrate the effectiveness of our approach.