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. However, most current data augmentation implementations applied in meta-learning are the same as those used in the conventional image classification. In this paper, we introduce a new data augmentation method for meta-learning, which is named as ``Task Level Data Augmentation'' (referred to Task Aug). The basic idea of Task Aug is to increase the number of image classes rather than the number of images in each class. In contrast, with a larger amount of classes, we can sample more diverse task instances during training. This allows us to train a deep network by meta-learning methods with little over-fitting. Experimental results show that our approach achieves state-of-the-art performance on miniImageNet, CIFAR-FS, and FC100 few-shot learning benchmarks. Once paper is accepted, we will provide the link to code. Although the machine learning systems have achieved a human-level ability in many fields with a large amount of data, learning from a few examples is still a challenge for modern machine learning techniques. Recently, the machine learning community has paid significant attention to this problem, where few-shot learning is the common task for meta-learning (e.g., Ravi & Larochelle (2017) ; Finn et al. (2017) ; ; Snell et al. (2017) ). The purpose of few-shot learning is to learn to maximize generalization accuracy across different tasks with few training examples. In a classification application of the few-shot learning, tasks are generated by sampling from a conventional classification dataset; then, training samples are randomly selected from several classes in the classification dataset. In addition, a part of the examples is used as training examples and testing examples. Thus, a tiny learning task is formed by these examples. The meta-learning methods are applied to control the learning process of a base learner, so as to correctly classify on testing examples. Data augmentation is widely used to improve the training of deep learning models. Usually, the data augmentation is regarded as an explicit form of regularization He et al. (2016) ; Simonyan & Zisserman (2014) ; . Thus, the data augmentation aims at artificially generating the training data by using various translations on existing data, such as: adding noises, cropping, flipping, rotation, translation, etc. The general idea of data augmentations is increasing the number of data by change data slightly to be different from original data, but the data still can be recognized by human. The new data involved in the classes are identical to the original data. However, the minimum units of meta-learning are the tasks rather than data. Increasing the data of original class cannot increase the types of task instances. Therefore, "Task Aug" increases the data that can be clearly recognized as the different classes as the original data. With novel classes, the more diverse task instances can be generated. This is important for the meta-learning, since metalearning models must predict unseen classes during the testing phase. Therefore, a larger number of classes is helpful for models to generate task instances with different classes. In this work, the natural images are augmented by being rotated 90, 180, 270 degrees (we show examples in Figure 1 ). We compare two cases, 1) the new images are converted to the classes of original images and 2) the new images are separated to the new classes. The proposed method is evaluated by experiments with the state of art meta-learning Methods Snell et al. (2017) (2017) . The experimental result analysis shows that Task Aug can reduce over-fitting and improve the performance, while the conventional data augmentation (referred to Data Aug) of rotation, which converts the novel data into the classes of original data, does not improve the performance and even causes the worse result. In the comparative experiments, Task Aug achieves the best accuracy of the meta-learning methods applied. Besides, the best results of our experiments exceed the current state-of-art result over a large margin.
We propose a data augmentation approach for meta-learning and prove that it is valid.
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