  In recent years, deep learning has achieved remarkable achievements in many
fields, including computer vision, natural language processing, speech
recognition and others. Adequate training data is the key to ensure the
effectiveness of the deep models. However, obtaining valid data requires a lot
of time and labor resources. Data augmentation (DA) is an effective alternative
approach, which can generate new labeled data based on existing data using
label-preserving transformations. Although we can benefit a lot from DA,
designing appropriate DA policies requires a lot of expert experience and time
consumption, and the evaluation of searching the optimal policies is costly. So
we raise a new question in this paper: how to achieve automated data
augmentation at as low cost as possible? We propose a method named BO-Aug for
automating the process by finding the optimal DA policies using the Bayesian
optimization approach. Our method can find the optimal policies at a relatively
low search cost, and the searched policies based on a specific dataset are
transferable across different neural network architectures or even different
datasets. We validate the BO-Aug on three widely used image classification
datasets, including CIFAR-10, CIFAR-100 and SVHN. Experimental results show
that the proposed method can achieve state-of-the-art or near advanced
classification accuracy. Code to reproduce our experiments is available at
https://github.com/zhangxiaozao/BO-Aug.
