ADVANCEMENTS IN GAN MODELS:  A STUDY OF KEY VARIANTS AND EVALUATION METRICS

Nguyen Mau Truong Giang; Van-Hau Nguyen

Nguyen Mau Truong Giang VNPT – AI
Van-Hau Nguyen Hung Yen University of Technology and Education

Keywords: Generative Adversarial Networks (GANs), Generative models, Evaluation metrics, Model stability, Loss functions, Machine Learning Applications

Abstract

This study presents a comprehensive analysis of Generative Adversarial Networks (GANs), focusing on their transformative role since their inception in 2014. Emphasizing game-theoretical principles, GANs mark a significant shift in generative modeling, predominantly applied in diverse fields from computer vision to data science. Our research rigorously investigates the evolutionary advancements in GAN architectures and evaluation metrics, particularly addressing the enhancement of data quality and the resolution of training instabilities. We delve into various GAN derivatives, including Conditional GANs, Wasserstein GANs, CycleGANs, and StyleGANs, exploring their unique contributions to improved modeling performance. This paper highlights the extensive applications of these models and their impact in practical scenarios. Additionally, we address current challenges within the GAN domain and suggest potential future research directions. Our work provides a concise yet comprehensive overview of GAN frameworks, underscoring their ongoing evolution and relevance in modern machine learning.

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