Understanding Data Augmentation for Classification: When to Warp?
In this paper we investigate the benefit of augmenting data with synthetically created samples when training a machine learning classifier. Two approaches for creating additional training samples are data warping, which generates additional samples through transformations applied in the data space, and synthetic over sampling, which creates additional samples in feature space. We experimentally evaluate the benefits of data augmentation for a convolutional backpropagation trained neural network, a convolutional support vector machine and a convolutional extreme learning machine classifier, using the standard MNIST handwritten digit dataset. We found that while it is possible to perform generic augmentation in feature space, if plausible transforms for the data are known then augmentation in data space provides a greater benefit for improving performance and reducing overfitting.
2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA)