Increasing the Classification Accuracy in Deep Learning using Generative Adversarial Networks

Abstract

In recent years, Generative Adversarial Networks (GANs) have become a research focus of deep learning. Several applications and models have been developed using GANs to make the training process more optimized and accurate. The consistency of training data sets is one of the core features of machine learning algorithms. Several models fail to achieve higher accuracies due to inconsistency in datasets. In this proposed work we discuss how the machine learning model on images varies its performance with data, and introduce a new approach for generating training datasets of images using generative models. GANs are particular kind of unsupervised Neural Networks which use differentiable randomness to generate photo-realistic images. The developed GAN architecture automatically adds the randomness to the outputs making the images as realistic as possible. All our manipulations to the GAN architecture are expressed concerning necessitated optimisation and are used on near-real time. The new datasets are generalized with different network architectures without any prior additions and are considered as high quality in case of the generated model.