Comparative Study on Generative Adversarial Networks
This is an incremental study that synthesizes existing research for researchers and practitioners interested in GANs, offering a comparative overview rather than novel advancements.
The paper tackles the problem of understanding and comparing various Generative Adversarial Network (GAN) models by studying the original framework and its modifications, providing a comparative analysis without presenting new experimental results or concrete numbers.
In recent years, there have been tremendous advancements in the field of machine learning. These advancements have been made through both academic as well as industrial research. Lately, a fair amount of research has been dedicated to the usage of generative models in the field of computer vision and image classification. These generative models have been popularized through a new framework called Generative Adversarial Networks. Moreover, many modified versions of this framework have been proposed in the last two years. We study the original model proposed by Goodfellow et al. as well as modifications over the original model and provide a comparative analysis of these models.