Experimental Assessment of the Performance of Data Augmentation with Generative Adversarial Networks in the Image Classification Problem

Deep Learning algorithms have almost become a key standard for majority of vision and machine learning problems. Despite its common usage and high performance for many applications, they have certain disadvantages. One major problem with deep learning methods is the size of the dataset to be used for training. The methods require a large dataset for an adequate training. However, a large dataset may not be available for all problems. In such a case, researchers apply data augmentation methods to obtain a larger dataset from a given dataset. For the image classification problem, conventional method for data augmentation is the application of transformation based methods; such as flipping, rotation, blurring etc. Recently, generative models which apply deep learning methods are also commonly used for data augmentation. On the other hand, in case of a too large dataset the classifiers may overfit and end up with a lack of generalization. In this study, we explore the usage of generative adversarial networks for data augmentation in the image classification problem. We evaluate the classification performance with three types of augmentation methods. Dataset is first augmented by two conventional methods; Gaussian blurring and dropout of regions, then by generative adversarial networks. Meanwhile, we observe the behavior of the classifier for various sized datasets with and without data augmentation. We observe that especially in datasets of certain sizes generative adversarial networks can be effectively used for data augmentation.