GAN Data Augmentation Through Active Learning Inspired Sample Acquisition

Christopher Nielsen, Michal Okoniewski; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2019, pp. 109-112

Data augmentation is frequently used to increase the effective training set size when training deep neural networks for supervised learning tasks. This technique is particularly beneficial when the size of the training set is small. Recently, data augmentation using GAN generated samples has been shown to provide performance improvement for supervised learning tasks. In this paper we propose a method of GAN data augmentation for image classification that uses the prediction uncertainty of the classifier network to determine the optimal GAN samples to augment the training set. We apply the acquisition function framework originally developed for active learning to evaluate the sample uncertainty. Preliminary experimental results are provided to demonstrate the benefit of this technique.