  Data augmentation is one of the most effective approaches for improving the
accuracy of modern machine learning models, and it is also indispensable to
train a deep model for meta-learning. In this paper, we introduce a task
augmentation method by rotating, which increases the number of classes by
rotating the original images 90, 180 and 270 degrees, different from
traditional augmentation methods which increase the number of images. With a
larger amount of classes, we can sample more diverse task instances during
training. Therefore, task augmentation by rotating allows us to train a deep
network by meta-learning methods with little over-fitting. Experimental results
show that our approach is better than the rotation for increasing the number of
images and achieves state-of-the-art performance on miniImageNet, CIFAR-FS, and
FC100 few-shot learning benchmarks. The code is available on
\url{www.github.com/AceChuse/TaskLevelAug}.
