Comparative Study on Generative Adversarial Networks

Abstract

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.

8 pages, 7 figures

References in corpus (7)

• Conditional Generative Adversarial Nets
• Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
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• InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
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Cited by in corpus (5)

• Generative Adversarial Networks in Computer Vision: A Survey and Taxonomy
• A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications
• Generative Adversarial Networks (GANs): What it can generate and What it cannot?
• Concept-Oriented Deep Learning: Generative Concept Representations
• Game of GANs: Game-Theoretical Models for Generative Adversarial Networks