Episodic-free Task Selection for Few-shot Learning
Tao Zhang
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.