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Data augmentation is an essential part of the training process applied todeep learning models. The motivation is that a robust training process for deeplearning models depends on large annotated datasets, which are expensive to beacquired, stored and processed. Therefore a reasonable alternative is to beable to automatically generate new annotated training samples using a processknown as data augmentation. The dominant data augmentation approach in thefield assumes that new training samples can be obtained via random geometric orappearance transformations applied to annotated training samples, but this is astrong assumption because it is unclear if this is a reliable generative modelfor producing new training samples. In this paper, we provide a novel Bayesianformulation to data augmentation, where new annotated training points aretreated as missing variables and generated based on the distribution learnedfrom the training set. For learning, we introduce a theoretically soundalgorithm --- generalised Monte Carlo expectation maximisation, and demonstrateone possible implementation via an extension of the Generative AdversarialNetwork (GAN). Classification results on MNIST, CIFAR-10 and CIFAR-100 show thebetter performance of our proposed method compared to the current dominant dataaugmentation approach mentioned above --- the results also show that ourapproach produces better classification results than similar GAN models.

A Bayesian Data Augmentation Approach for Learning Deep Models