  The success of training deep Convolutional Neural Networks (CNNs) heavily
depends on a significant amount of labelled data. Recent research has found
that neural style transfer algorithms can apply the artistic style of one image
to another image without changing the latter's high-level semantic content,
which makes it feasible to employ neural style transfer as a data augmentation
method to add more variation to the training dataset. The contribution of this
paper is a thorough evaluation of the effectiveness of the neural style
transfer as a data augmentation method for image classification tasks. We
explore the state-of-the-art neural style transfer algorithms and apply them as
a data augmentation method on Caltech 101 and Caltech 256 dataset, where we
found around 2% improvement from 83% to 85% of the image classification
accuracy with VGG16, compared with traditional data augmentation strategies. We
also combine this new method with conventional data augmentation approaches to
further improve the performance of image classification. This work shows the
potential of neural style transfer in computer vision field, such as helping us
to reduce the difficulty of collecting sufficient labelled data and improve the
performance of generic image-based deep learning algorithms.
