Stride Random Erasing Augmentation
Teerath Kumar, Rob Brennan, Malika Bendechache
Abstract
This paper presents a new method for data augmentation called Stride Random Erasing Augmentation (SREA) to improve classification performance. In SREA, probability based strides of one image are pasted onto another image and also labels of both images are mixed with the same probability as the image mixing, to generate a new augmented image and augmented label. Stride augmentation overcomes limitations of the popular random erasing data augmentation method, where a random portion of an image is erased with 0 or 255 or the mean of a dataset without considering the location of the important feature(s) within the image. A variety of experiments have been performed using different network flavours and the popular datasets including fashion-MNIST, CIFAR10, CIFAR100 and STL10. The experiments showed that SREA is more generalized than both the baseline and random erasing method. Furthermore, the effect of stride size in SREA was investigated by performing experiments with different stride sizes. Random stride size showed better performance. SREA outperforms the baseline and random erasing especially on the fashion-MNIST dataset. To enable the reuse, reproduction and extension of SREA, the source code is provided in a public git repository https://github.com/kmr2017/stride-aug.