Comparative Analysis Of DCGAN And WGAN

Abstract

In current years, image recognition is increasingly being used, however, it becomes much less correct if there is not plenty data available while training data. Generative Adversarial Networks (GAN) can assist to create new data that is nearly similar to the original data to help the training process while the original data isn't always much in order that the training process might be more accurate. GAN is currently growing and there are an increasing number of types, along with the Deep Convolutional GAN (DCGAN) and Wasserstein GAN (WGAN) algorithms. This study analyzes the comparison among DCGAN and WGAN which objectives to offer a decision approximately which algorithm is better to use. Based on the research results, DCGAN is simpler however there are still drawbacks, particularly the Mode collapse and vanishing gradient, at the same time as WGAN can remedy those shortcomings however the process is slower.