  Few-Shot classification aims at solving problems that only a few samples are
available in the training process. Due to the lack of samples, researchers
generally employ a set of training tasks from other domains to assist the
target task, where the distribution between assistant tasks and the target task
is usually different. To reduce the distribution gap, several lines of methods
have been proposed, such as data augmentation and domain alignment. However,
one common drawback of these algorithms is that they ignore the similarity task
selection before training. The fundamental problem is to push the auxiliary
tasks close to the target task. In this paper, we propose a novel task
selecting algorithm, named Optimal Transport Task Selecting (OTTS), to
construct a training set by selecting similar tasks for Few-Shot learning.
Specifically, the OTTS measures the task similarity by calculating the optimal
transport distance and completes the model training via a self-supervised
strategy. By utilizing the selected tasks with OTTS, the training process of
Few-Shot learning become more stable and effective. Other proposed methods
including data augmentation and domain alignment can be used in the meantime
with OTTS. We conduct extensive experiments on a variety of datasets, including
MiniImageNet, CIFAR, CUB, Cars, and Places, to evaluate the effectiveness of
OTTS. Experimental results validate that our OTTS outperforms the typical
baselines, i.e., MAML, matchingnet, protonet, by a large margin (averagely
1.72\% accuracy improvement).
