Comparative Study on Generative Adversarial Networks

2018/01/12 by Saifuddin Hitawala, Hitawala, Saifuddin
Computer Science · #Artificial Intelligence (cs.AI) #Digital Media Forensic Detection #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #Machine Learning (cs.LG) #Music and Audio Processing

paper · pdf · doi:10.48550/arxiv.1801.04271

openalex publication_date 2018/01/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• Adversarial Feature Learning

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